Let’s take a journey through some of these remarkable stories.

Diagnosing Diseases in Rural Clinics – Nigeria

In Nigeria, startups like MDAAS Global and health tech platforms are using AI-driven diagnostics to provide faster, cheaper, and more accurate medical testing in underserved areas. AI tools analyse patient symptoms and lab data to flag early signs of chronic diseases, empowering health workers who may not have access to specialist doctors.

Why it matters: In places where one doctor serves thousands, AI can serve as a second brain, saving time, costs, and lives.

Smart Farming & Crop Predictions – Kenya and Ghana

In East and West Africa, AI-powered platforms like UjuziKilimo (Kenya) and Esoko (Ghana) are helping farmers improve yields. These tools use satellite data and machine learning to forecast rainfall, identify soil deficiencies, and suggest optimal planting seasons.

Why it matters: With climate change disrupting traditional farming patterns, AI is becoming a quiet agricultural ally.

Bridging Educational Gaps – South Africa

South African edtech company Ubongo and chatbot-based platforms are leveraging AI to deliver personalised, multilingual content to students in schools in marginalised communities. Some bots even adapt their learning pace and style to each child’s performance.

Why it matters: In a continent where teacher shortages are common, AI can supplement learning, making education more accessible and inclusive.

Tracking Human Rights Violations – Cameroon & Sudan

AI is also being used to uphold justice. In conflict zones like Cameroon and Sudan, image recognition tools and satellite surveillance help identify burned villages or illegal detentions. Organisations like Amnesty International and Global Rights are integrating AI into their advocacy work.

Why it matters: When traditional media is silenced, AI can serve as a witness.

The Rise of Local AI Ecosystems

From Data Science Nigeria to Zindi Africa, local ecosystems are training the next generation of African AI talent. These platforms host competitions and bootcamps that encourage young Africans to solve problems using real-world data, from predicting power outages to improving traffic systems.

Why it matters: Empowering local AI talent ensures the technology serves local realities, not just imported frameworks.

Final Thoughts

While debates rage globally about AI replacing jobs or threatening privacy, on the African continent, AI’s narrative is refreshingly human. It’s about reaching the unreached and amplifying local voices and filling systemic gaps.

But we must remain vigilant. These tools must be built ethically, inclusively, and in partnership with the communities they aim to serve.

The question isn’t whether AI will shape Africa’s future.
The question is: Who will build it, and who will benefit?

Let’s make sure the answer includes all of us.

References

1. State of AI in Africa Report 2023
   https://cipit.strathmore.edu/wp-content/uploads/2023/12/Final-Report-The-State-of-AI-in-Africa-Report-2023-3.pdfchrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://cipit.strathmore.edu/wp-content/uploads/2023/12/Final-Report-The-State-of-AI-in-Africa-Report-2023-3.pdf
   Detailed analysis of how AI is impacting sectors across African countries, plus data on ecosystem growth

2. How AI Is Revolutionising Healthcare in Africa
   https://www.weforum.org/publications/centre-for-the-fourth-industrial-revolution-network-2023-2024-impact-report/
   Features case studies from Nigeria and Rwanda.

3. MDaaS Global – Building Africa’s Clinical Infrastructure

   https://www.mdaas.io/
   Example of a well-adapted AI-powered diagnostic system in Nigeria.

4. UjuziKilimo (Kenya) and Esoko (Ghana)
   https://www.ujuzi.africa/
   https://esoko.com/

   Two major agri-tech platforms use AI and data analytics for soil and weather intelligence.

5. Ubongo – Africa’s Leading Edutainment Company
   https://www.ubongo.org/

   Though not exclusively AI-based, they are exploring adaptive learning powered by machine learning for children across Africa.

6. AI and Education in Africa
   https://iafrica.com/revolutionizing-education-how-ai-powered-platforms-are-transforming-learning-in-africa/#:~:text=Similarly%2C%20Ubongo%2C%20a%20pan%2D,is%20a%20masterclass%20in%20scalability.
   Discusses AI tools improving education outcomes in underserved communities.

7. Decoding Conflict with AI: How Amnesty International Is Using Tech to Protect Human Rights
   https://www.humanrightsresearch.org/post/harnessing-technology-to-safeguard-human-rights-ai-big-data-and-accountability#:~:text=Organizations%20like%20Amnesty%20International%20use,%2C%20algorithmic%20bias%2C%20and%20censorship. Covers the use of machine learning to detect human rights violations in Sudan and Cameroon.

8. AI for Peace and Justice in Africa
   https://ict4peace.org/what-we-do/#ict-for-peaceful-purposes
   Explores digital tools, including AI, in promoting peacebuilding in African conflict regions.

9. Data Science Nigeria
   https://www.datasciencenigeria.org/

   One of Africa’s leading AI education and research nonprofits, offering bootcamps and AI competitions.

10. Zindi – Africa’s Data Science Community
    https://zindi.africa

    AI competitions focused on solving real African problems with data.
    
    

In this article, we examine how creative professionals are adapting their workflows, mindsets, and skill sets to remain relevant and inspired in an increasingly AI-driven world.

AI Is Not Replacing Designers: It's Redefining the Role

Let’s be clear: AI can generate images, suggest layouts, or even write design briefs. But it cannot replace human intuition, storytelling, or emotional intelligence. What it can do is help automate repetitive tasks and enhance ideas.

Designers are becoming “AI conductors”, curating, prompting, and refining AI outputs rather than creating every pixel from scratch.

Example:
Tools like Midjourney, Adobe Firefly, and Runway ML enable designers to rapidly generate moodboards, concept art, or variations, freeing up time for deeper, more creative thinking.

How Designers Are Using AI

Here are some ways creatives are integrating AI into their workflows:

1. Rapid Prototyping

AI tools generate early-stage ideas or mockups, speeding up the iteration process.

Try: Figma’s AI plugins or Uizard for instant wireframes and UI design.

2. Content-Aware Design

AI is now embedded in tools like Adobe Photoshop's Generative Fill or Canva’s Magic Design, which auto-suggest layouts and imagery based on your input.

3. Personalised Branding

With AI, designers can create dynamic, adaptive branding for different platforms and audiences.

The Shift in Skills: What Designers Are Now Learning

As the design landscape evolves, so must the designer. Here’s what’s becoming more important:

• Prompt Engineering: Crafting effective AI prompts is an art in itself.

• Critical Curation: Knowing what AI-generated output to keep and what to toss.

• Ethical Design Thinking: Being mindful of bias, plagiarism, and authenticity.

• Human-AI Collaboration: Blending intuition with computation.
  
  

Pro Tip: Platforms like Domestika and FutureLearn now offer courses on AI for creatives.

What Creatives Are Saying

“AI helps me visualise and iterate faster, but it’s still my voice that drives the final product.”
— Lola Akinmade Åkerström, Designer & Visual Storyteller

“It’s not about fearing the tool, it’s about mastering it.”
— David Ariev, 3D Artist and AI Experimenter

The Future: Creative Direction, Not Just Creation

As AI becomes more embedded in design workflows, creative professionals are evolving into strategic thinkers, storytellers, and visionaries. The most successful designers in this new era are those who:

• Embrace AI as a co-creator

• Stay curious and adaptable

• Bring a distinctively human perspective to every project.
  
  

Finally, AI isn't the end of creativity; it’s an evolution. The design world isn’t getting less human; it’s getting more augmented, more imaginative, and more accessible.

Whether you're a graphic designer, brand strategist, or digital artist, your most valuable asset is still your creative judgment. Learn to work with AI, not against it.

Resources & References

• Generative is About to Upend the Way Designers Work

• The Future of Creativity

• Canva Magic Studio

• Runway ML – Creative AI Tools

• Figma AI Plugins
  
  

From marketing to HR, AI is transforming how everyday professionals work. As tools get smarter, non-tech roles are evolving in real-time, shifting responsibilities, boosting productivity, and changing skill requirements.

Here are five non-technical roles that AI is reshaping and what you need to know to stay ahead.

1. Marketing Professionals: AI-Enhanced Storytellers

AI is no longer just optimising ads, it’s helping craft them.

Marketing professionals now rely on tools like ChatGPT, Jasper, and Midjourney for content creation, ideation, A/B testing, and design generation. AI can write copy, analyse audience behaviour, and predict campaign performance with surprising accuracy.

What’s changing?

• Greater focus on strategy and brand positioning

• Less time spent writing or designing from scratch

• Need to learn prompt engineering and AI tool usage

Pro tip: Get comfortable with creative AI tools. Knowing how to guide AI will be more important than writing every line yourself.

2. Customer Service Agents: AI-Augmented Problem Solvers

AI chatbots now handle basic queries, FAQs, and transactional issues. This frees up human agents to handle complex, emotional, or nuanced concerns.

But AI doesn’t replace empathy; it enhances it. Modern agents work alongside AI, using it to retrieve customer data, suggest solutions, and even predict problems before they arise.

What’s changing?

• Shift from reactive to proactive support

• Emphasis on soft skills like empathy and conflict resolution

• Integration with AI-powered CRMs and chat systems

Pro tip: Train in emotional intelligence and get familiar with tools like Zendesk AI or Intercom.

3. Administrative Assistants: AI Workflow Designers

From scheduling meetings to drafting emails, many tasks handled by administrative professionals can now be automated with AI.

Rather than becoming outdated, assistants are now AI workflow designers, curating which tools to use, automating tasks, and managing smarter calendars and communication systems.

What’s changing?

• Less manual scheduling, more process optimisation

• Use of AI tools like Reclaim.ai, Notion AI, or x.ai

• More strategic support and decision-making tasks

Pro tip: Learn to build automations using tools like Zapier or Notion with AI plugins

4. HR Professionals: Talent Experience Architects

Hiring, onboarding, and employee engagement are now powered by data-driven insights. AI can screen resumes, assess personality fit, and even forecast employee turnover.

This shifts HR into a more strategic role, designing inclusive, engaging, and growth-oriented experiences for talent that leverage AI insights.

What’s changing?

• Emphasis on ethical AI and bias mitigation

• Use of AI for talent analytics and predictive hiring

• Blending human warmth with data-driven decision-making

Pro tip: Explore AI-enabled platforms like Pymetrics or HireVue, but always balance data with human judgment.

5. Teachers & Educators: AI-Guided Learning Designers

Education is going through a quiet revolution. AI can now personalise learning paths, assess student progress in real-time, and provide targeted tutoring support.

This doesn’t replace educators; it frees them to focus more on mentorship, critical thinking, and designing more inclusive learning experiences.

What’s changing?

• Shift from “sage on the stage” to “guide on the side”

• Use of AI in curriculum design and learning management systems

• Higher demand for adaptability and digital fluency

Pro tip: Try tools like Khanmigo (by Khan Academy) or Google’s AI in education suite to stay ahead.

Final Thoughts

AI isn't just reshaping tech jobs, it's reimagining every job.

Whether you're in customer service, education, or HR, the key is not to fear AI but to understand how to work with it. Upskilling, learning to use AI tools, and focusing on human strengths like creativity, empathy, and strategy will help you thrive in this new era.

You don’t have to be a coder to benefit from AI; you just have to be curious, adaptable, and open to learning.

The response was thoughtful, gentle. And for a moment, it helped. Not just because it gave me words, but because it reminded me how hard it can be to be human, and how strange it is to turn to a machine for guidance in a moment of grief.

It made me wonder: Can machines understand empathy?

What Empathy Means to Me

To me, empathy is more than just saying “I understand.” It’s the quiet sense that someone sees you, that they’re willing to step into your world for a moment. It’s often messy, imperfect, and deeply personal. That’s why the idea of AI showing empathy feels both fascinating and unsettling.

Machines don’t have hearts. They don’t get tired or heartbroken or thrilled beyond words. But they’re learning to recognise our emotions: the sigh in our voice, the sadness between our words, the patterns in how we type late at night.

Is That Enough?

Some AI tools are already helping people manage anxiety, loneliness, and even grief. I’ve seen people feel comforted just knowing that something, something is responding kindly. It’s not the same as talking to a friend, but it’s also not nothing.

Still, I wonder: is that real empathy, or just a well-trained echo?

What We’re Asking

Maybe the better question isn’t “Can AI feel?”, but “Should it try to?”

If we’re going to build machines that support us emotionally, we have to be intentional about how we train them, what values we embed, whose definitions of compassion they learn from, and how we protect the human part of connection.

Where My Heart Lands

I don’t think machines need to feel to be helpful. But I do think we need to stay in the driver’s seat when it comes to emotional intelligence. Empathy isn’t just data, it’s care, context, and choice.

And while AI might never cry with us, it might one day sit beside us in our silence, and maybe that’s a start.

I still think about that moment I asked a machine to help me express something so deeply human. Maybe it didn’t understand my grief, but it helped me show up in it. And in that, there was something quietly beautiful.

Here are some of the most fascinating AI-driven careers you probably didn’t know existed but might be perfect for you.

AI Ethicist & Bias Auditor

As AI systems increasingly influence hiring, policing, lending, and healthcare, the need for ethical oversight has exploded. AI Ethicists and Bias Auditors ensure algorithms are fair, transparent, and accountable. They combine ethics, sociology, law, and technical knowledge to help organisations build responsible AI systems.

Perfect for: Social scientists, philosophers, lawyers, and DEI professionals who want to work in tech.

Prompt Engineer & AI Interaction Designer

With the rise of generative AI tools like Chatgpt, Grammarly, Gemini, Midjourney, etc., there's a growing demand for experts who know how to "talk" to AI. Prompt Engineers design the questions, commands, and conversational flows that teach AI systems to perform at their best.

Perfect for Writers, UX designers, linguists, and creatives.

AI Trainer

Training AI isn’t just about data, it’s about feedback. AI Trainers work directly with machine learning models to reinforce correct outputs and flag errors. Whether it’s improving virtual assistants or autonomous vehicles, these trainers act as the human brain behind the machine mind.

Perfect for Teachers, quality analysts, and detail-oriented professionals.

4. Synthetic Media Designer

From creating virtual influencers to designing digital humans for films and marketing, synthetic media is big business. These designers blend animation, AI, and storytelling to craft hyper-realistic characters and content.

Perfect for: Filmmakers, digital artists, animators, marketers.

5. AI Policy & Governance Specialist

Governments and organisations need help navigating the fast-moving world of AI regulation. Specialists in this role track emerging legislation, assess compliance, and craft internal AI use policies.

Perfect for Policy analysts, legal researchers, and corporate compliance professionals.

6. AI Wellness Coach

Surprised? You shouldn't be. As AI creeps into everyday life, burnout and over-reliance have become real mental health concerns. AI Wellness Coaches help individuals and organisations create healthy boundaries, especially for teams working in high-tech environments or with AI-based productivity tools.

Perfect for mental health professionals, coaches, and HR professionals

7. AI Community Manager

As AI tools become platforms, communities around them are growing. AI Community Managers foster learning, support, and innovation within online and in-person AI communities; Curating content, hosting events, and gathering feedback that helps companies iterate fast.

Perfect for content creators, tech evangelists, and social media managers.

AI Forensics Expert

With the rise of deepfakes, misinformation, and cyberthreats, there is a need for forensic experts. AI Forensics experts investigate manipulated content, identify AI-generated material, and provide expert analysis in legal or journalistic contexts.

Perfect for: Investigative journalists, cybersecurity professionals, and digital analysts.

The Bottom Line

AI is no longer just a technical field; it’s a human one. These emerging careers prove that the future of work isn’t just about coding, but about creativity, responsibility, and collaboration with machines. Whether you’re from the arts, social sciences, or STEM, an AI career is likely waiting for someone like you.

Reference

https://reports.weforum.org/docs/WEF_Future_of_Jobs_Report_2025.pdf

https://www.ibm.com/think/insights/impact-of-ai

https://www.linkedin.com/pulse/5-new-jobs-being-created-ai-roc-search-qqy2e/

https://medium.com/@vaishnaviyada/ai-machine-learning-careers-in-2025-skills-and-growth-opportunities-fc284c1bf09d

https://www.ibm.com/think/topics/ai-ethics

https://www.mckinsey.com/mgi/our-research/generative-ai-and-the-future-of-work-in-america

https://www.accenture.com/us-en/insights/technology/technology-trends-2024

For some, AI might seem like a futuristic concept reserved for robots and high-tech labs. Still, the truth is, AI is already imprinted in the fabric of our everyday lives, often in ways we don’t even notice. From morning to night, these technologies streamline our routines, anticipate our needs, and personalise our experiences.

Let’s explore how AI quietly works behind the scenes to make your life easier, one task at a time.

Smartphones: Your Pocket-Sized AI Companion

Every time you unlock your phone with facial recognition, autocorrect a typo, or ask Siri or Google Assistant a question, you're tapping into AI. Facial recognition uses deep learning to recognise your features.
Predictive text and autocorrect rely on Natural Language Processing to understand what you’re trying to say.
Voice assistants interpret your voice commands and deliver responses using a mix of NLP and machine learning.

Streaming Platforms: Personalised to Perfection

Have you ever noticed how Netflix, Spotify, or YouTube tend to know what you want next? That’s AI in action. These platforms use collaborative filtering and user behaviour analysis to recommend content tailored to your tastes. Netflix has been kind enough to share details about these techniques on their tech blog.

Navigation and Ride-Sharing: Getting There Faster

Apps like Google Maps, Uber, and Bolt use AI to optimise travel. AI helps predict traffic patterns, suggests the fastest routes, and calculates ETA using real-time data.
Ride-hailing services rely on demand prediction algorithms to match drivers and riders efficiently. For instance, Uber’s ridesharing service seems simple on the surface: open the app, request a ride, get picked up, and arrive at your destination. But beneath this smooth user experience lies a complex web of Artificial Intelligence systems that enable real-time decision-making at scale.

Online Shopping: AI is your Personal Shopper

AI plays a big role in your digital shopping experience. According to McKinsey, Amazon’s recommendation engine is responsible for around 35% of its revenue.
Chatbots help answer your questions, recommend products, and even complete transactions without human help.

Banking & Finance: Safer and Smarter Transactions

Behind every secure financial transaction is some form of AI. Fraud detection uses machine learning to identify suspicious activity. Credit scoring and loan approvals are increasingly guided by AI systems that look at more than just your credit history.
Robo-advisors offer investment strategies tailored to your profile, often more affordably than human advisors.

Smart Homes and I ot: Living with AI

Is your smart thermostat learning your preferences? That’s AI doing the job.

Devices like Alexa, Google Nest, and Ring use machine learning and computer vision to adapt to your lifestyle. AI helps automate lighting, temperature, music, and security systems.

Email, Spam Filters, and Productivity Tools

Even your inbox is powered by AI. Spam filters use machine learning to detect unwanted or malicious content. Writing tools like Grammarly and Notion AI use NLP to help you express your thoughts clearly and professionally.

On a final note, it is important to know that AI isn’t just about robots or sci-fi movies. It’s in your phone, your home, your bank, your entertainment, and it is quietly shaping your daily experiences in ways you rarely notice.

As debates continue about AI ethics, jobs, and the future, it’s helpful to remember: AI is already here, making your life easier in small but powerful ways.

References

• https://www.ibm.com/topics/natural-language-processing

• https://netflixtechblog.com/

• https://www.uber.com/blog/engineering/ai/

• https://www.mckinsey.com/industries/retail/our-insights/the-future-of-personalization-and-ai-in-retail

• https://www.forbes.com/sites/forbestechcouncil/2021/06/24/ai-in-banking-how-artificial-intelligence-is-transforming-financial-services/

• https://builtin.com/articles/what-is-a-smart-home

I’m good for the first sentence, line, phrase, and even the first word.

Most of my creative ideas — poems, short stories, or screenplays — come to me as openers, sparks of language borne by emotion or daydreaming. The final product might take a different form or direction altogether but this is how my brain typically works — fragments and phrases come first, and then ideating and fleshing out follow.

Before the explosion of AI, whenever words and phrases did not come readily, my technological remedies were online prompts and topic/title generators. They helped to jump-start the process. When they failed, I relied on the old faithful of giving myself to days-long purposeful reading and or late-night strolls till I saw, smelt or felt something that triggered a lingual emotional expression. And if nothing happened, I agonised, went to bed and lived to agonise another day.

These days, with the current explosion of AI, especially the continuous improvements of Large Language Models, I have access to what seems like endless tools that can do the ideating, prompting and even the writing on my behalf. Artificial intelligence cannot only write code, it can also write poetry and prose to proficient and laudable results.

But I find it difficult to participate. It feels like cheating, a type of deft refusal to apply myself to the grime of creating, to the not-glamour of it. It feels like skipping the cooking process to make a meal. Besides, what is the point of a poem if it isn’t generated from the bowels of the poet?

I use AI for a wide range of tasks: proofreading and refining long emails before sending them, navigating the internet more effectively to access information that might be tricky to find with a simple Google search, etc. I believe AI is useful for these kinds of tasks — making life easier and helping manage time more efficiently.

I cannot however bring myself to make poetry with it. I believe poetry is first for the poet. It is an expression of individual reality, fantasy, or nightmare. And while all poems don’t have to be personal, they must be true. They must carry the author’s intention while being open to interpretation. Besides, I am deathly afraid of the ethical implications of using AI to write poems: feeding it samples of my work and asking it to craft newer poems based on certain prompts. To what end? I’m also frightened by the reality that AI, and it’s probably true, can write a poem better than I can.

A study conducted by the University of Pittsburgh found that non-expert poetry readers preferred AI-generated poems to those written by famous human poets. Participants were more likely to attribute AI-created poems to human authors and rated them higher in quality. Many believed the human-written poems were AI-generated due to their complexity. This follows the trend of the blurring lines between AI-generated images, paintings, music, videos (although to much slower extents) and man-made art. It begs the question of what makes for a good poem. And is the point of writing poems, writing good poems?

What remains to be seen and perhaps further research will reveal, is the extent to which AI can make poems with nuances of human beings. I wonder if AI can write a poem that emotes like the quiet and dreadful simplicity of Carlos William Carlos’ The Red Wheelbarrow. Can it mirror the divinity of Gerard Manley Hopkins’ The Windhover? Or create the raw anguish of Sylvia Plath’s Lady Lazarus. The answer is that I don’t know yet but God, I hope not.

Tomi is a poet and author of Falling Boy, a collection of poems about love, death, and a mother’s love.

First published on Medium by Stephanie 'S.I' Ohumu on March 27, 2025.

Canonical link

Exported from Medium on April 22, 2025.

Executive Summary

Journalism in Sub-Saharan Africa faces many challenges. Most newsrooms have little money and few resources to work with. Journalists often worry about being censored or punished for their work. The many languages spoken in the region make it hard to fact-check stories across all communities. Old technology and a lack of modern tools make it difficult for journalists to do their job well. At the same time, false information spreads quickly on social media. All these problems make it hard, but very important, to check facts quickly and correctly. This situation shows why new solutions that fit the region’s needs are so important.

This case study examines the development and implementation of “MyAIFactChecker,” an AI-powered tool designed to enhance fact-checking capabilities in the region. Launched by Fact Check Africa, a Brain Builders Youth Development Initiative (BBYDI) initiative, this tool represents a significant step towards combating misinformation and strengthening journalistic integrity in Sub-Saharan Africa. Abideen Olasupo, the Global Director of BBYDI, said the initiative is a product of months of dedicated efforts and research with support from BECERA and the U.S. State Department.

Ideation and Initial Development

In 2023, BBYDI identified the need for an innovative solution to address these challenges. Alamin Musa Magaga, the product development lead, was brought on board to spearhead the technical development.

As Magaga recalls: “I was contacted by the global director of Brain Builders Youth Development Initiative, BBYDI, Olasupo Abideen, to facilitate the development of an AI solution for fact-checking.”

The project was initially conceived in anticipation of the U.S. West African Tech Challenge, where BBYDI aimed to present strategies to combat disinformation in West Africa.

Data Sources and AI Integration

MyAIFactChecker combines advanced search APIs, fact-checking databases, and a curated list of credible sources. Magaga explains the process:

“When the user inputs the claim, the first thing the tool will do is utilise the advanced search API, which will then go through a lot of credible resources and fact-checking databases and then it will check information related to that specific claim. After fetching the top sources, it’ll then utilise Generative AI to provide the response for the claim”

The system incorporates the OpenAI GPT-4 and LlaMA 3 model for efficient analysis and responses.

Recognising that many top news sources weren’t locally relevant, the team took steps to include reputable local sources. This approach ensured that the fact-checking process was grounded in locally relevant and trusted sources, enhancing the tool’s effectiveness in the West African context.

Pilot Implementation and Accuracy Benchmarking

The team at MyAIFactChecker places a high priority on accuracy, recognising its critical importance in journalism and fact-checking. Their approach to ensuring accuracy has evolved.

Initially, the team took a conservative approach by limiting the tool to only verifying information that had already been fact-checked by reputable sources. Magaga explains this early strategy:

“We decided to focus only on already fact-checked data, limiting our AI system to be able to fully respond only to questions that had been fact-checked by the organisation [Fact Check Africa].”

This cautious approach ensured high accuracy but limited the tool’s scope. More importantly, it was rooted in their primary goal, as Magaga emphatically states: “We had to optimise for correctness. It had limited functionalities, but our goal was to build a tool to curb misinformation, and not contribute to fake news, therefore, we had to push for 100% accuracy.”

As the project progressed, they expanded their capabilities while maintaining this strong focus on accuracy. Magaga describes their ongoing efforts: “We needed to monitor the tool for hallucinations because this is very common with generative AI. We are working on a monitoring system that helps us monitor the hallucination rate and manage it.”

This evolution from a system based solely on pre-verified information to a more comprehensive tool that can handle a wider range of queries, while still prioritising accuracy, demonstrates the team’s dedication to creating a reliable fact-checking resource. Their ongoing work on managing and mitigating AI hallucinations shows a nuanced understanding of the challenges posed by generative AI in the context of fact-checking.

Testing and Launch

The development, testing, and launch of MyAIFactChecker was an iterative process that involved continuous feedback and improvement. The initial development of MyAIFactChecker was a focused effort. The first four to five months were intensive and involved the development of the core functionality that was used in the U.S. West African Tech Challenge.

Following the initial development, the team extensively tested and refined the tool. Magaga describes the process:

“After the competition, we started working on the original version. The UI/UX team had to design the user interface, and then we started developing, after which people tested it and gave their feedback. Then, continuous iterations until the product was ready for launch.”

The team placed a strong emphasis on user feedback, both during development and after launch. Magaga explains: “We had a lot of feedback, much of which we couldn’t incorporate.” This highlights the challenges of balancing user requests with technical feasibility and project scope.

MyAIFactChecker was launched in April 2024, but this wasn’t the end of the development process. The team actively sought feedback even during launch, wanting to know what problems users had and how to fix them.

This continuous improvement and user engagement approach has contributed to the tool’s adoption and effectiveness.

Challenges and Solutions

• Resource Constraints

Building an AI-powered tool like MyAIFactChecker is capital-intensive, especially for a small media team. The team faced significant resource constraints but employed various strategies to overcome these challenges.

Before securing external support, the team focused on finding cost-effective solutions. Magaga explains their initial approach:

“We looked at a lot of alternatives and found something cheaper or even free, sometimes even better than the paid solutions or APIs that exist. We understood our capacity and had to focus on that.”

This strategy of seeking out affordable or free alternatives demonstrates the team’s resourcefulness in the face of financial constraints.

As the project progressed, the team successfully leveraged startup support programs to access necessary resources. Magaga shares:

“We applied for the Microsoft Startup Founders program, and they gave us access to different tools, which are part of the tools we are using, and now we’re also part of the Google Ads rate tool startup program, which also gives us at least $100,000 Google credit for different tools and resources”

These programs provided crucial support, allowing the team to access advanced tools and services that might otherwise have been out of reach.

The team had to make strategic decisions about which features to implement based on available resources, with a strong preference for existing libraries and open-source solutions instead of building from scratch.

This approach of carefully weighing the costs and benefits of different tools and features allowed the team to maximise their limited resources. The team’s approach to resource management evolved. The team’s efforts to secure resources and manage costs have contributed to the project’s sustainability.

• Infrastructure Challenges

Power outages are a common issue in the region. Magaga describes a workaround employed in the very early stages of his career: “Power is a problem because when you wake up every day, you have to think of the best place to go, sometimes we’d have power issues in our area, and I’ll have to be at the office overnight and sleep there.”

• Data Limitations

The team faced challenges in building predictive models due to limited datasets. Magaga explains: “We wanted to build a simple machine learning model for detecting and predicting fake news, but we understand how limited this can be because the sources play a big role in trying to determine whether a piece of information is accurate or not. That is very problematic in the real-world aspect of Nigeria, where we have limited data sets.”

To address this, they created a database to store fact-checked information and sources, building a foundation for future machine learning models. “But we have a database to store all the information that’s being fact-checked and their sources. This means we now have sources for a data set, and then we can build machine learning models for predicting fake news, and other research purposes”, he says.

Impact and Usage

Since its launch in early 2024, MyAIFactChecker has processed over 10,000 queries, especially during the August 2024 Nigerian protests. They have also trained over 12,000 students in universities on fact-checking Information.

Ethical Considerations

The team prioritises accuracy to avoid contributing to misinformation. Magaga emphasises: “We had to optimise for correctness. The tool incorporates generative AI to analyse claims and generate responses based on the gathered information.”

Lessons Learned

1. Prioritise Accuracy: In journalism, and as an organisation that tackles fake news, maintaining 100% accuracy is crucial.

2. Leverage Existing Tools: Instead of building everything from scratch, Magaga advises the utilisation and integration of existing APIs and libraries when possible, to come up with a solution that matches the organisation’s needs.

3. Continuous Learning: Stay updated with the latest AI tools and techniques. Magaga notes: “Each and every day, a lot of tools are coming to make this much easier, but it can also make the solutions you have implemented obsolete. Then you have to remove all the time and effort you spent, and just replace it with something better. But you just have to find a way to keep yourself updated for a more effective experience.”

4. Localisation is Key: Customising sources and content for specific regions enhances the tool’s relevance and effectiveness. The team created a custom list of top sources for Nigeria and other West African countries.

5. Feedback Loop:

Incorporate user feedback and continuous monitoring. Magaga mentions: “We had a lot of feedback, much of which we couldn’t incorporate. But when we get feedback and people tell us all the things they want us to implement, they’ll tell us what to remove and what to add. This was a hard decision, especially when you have a complete platform. But we take it all into account for the next version. We implement some of the changes, but we don’t do what people suggest all the time.” When it comes to resistance, it’s the ability to say no and manage expectations, especially from non-technical team members. “We know the consequences and what it’ll look like.”

“Whatever you build, people have to test it. From my perspective, it’s about the expectations. Are we able to build something that’ll satisfy what the journalists want? The recommendations and feedback are quite important. When we released the first version, people tested it, we got feedback, and had to go back to the drawing table, and do it all over again.”

“It’s what the customer wants, and you as the solution provider, have to do whatever it takes to satisfy their needs. Even during the launch, we had people provide feedback because we wanted to understand the position of the user. From the user, we can understand what is wrong with the program and how we can improve it. We also have a feedback box on the app. Feedback is very important, and we have it recurring.”

Conclusion

MyAIFactChecker represents a significant step forward in combating misinformation in Sub-Saharan Africa. By leveraging AI technologies and focusing on local needs, it offers a promising solution to the challenges faced by journalists in the region.

Magaga’s final thoughts encapsulate the project’s vision: “Artificial Intelligence is the future, the choice is now just up to the journalists, whether they’re going to use it or not. It’s not just fact-checking, it’s the storytelling, animation, video and image editing. It’s just part of the future and it has now started changing different aspects of journalism. It’s going to be there, things will be different and it’s going to change almost everything. People just have to find a way to be able to adapt and learn how to use it.”

This case study highlights the importance of innovation, adaptability, and collaboration in developing AI solutions for journalism in Sub-Saharan Africa. As the project continues to evolve, it has the potential to significantly impact the landscape of fact-checking and journalism in the region.

Thanks to Aisha Bello for support with research and copy.

First published on Medium by Stephanie S.I. Ohumu on January 30, 2025.

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Exported from Medium on April 22, 2025.

Dubawa Audio is an innovative AI-powered tool developed by the Centre for Journalism Innovation and Development (CJID) to address the challenges of monitoring and fact-checking radio content in sub-Saharan Africa. This case study explores the journey from ideation to implementation, highlighting key lessons for small newsrooms in the region looking to adopt AI solutions.

Background

Radio is the primary mass media in Africa, especially in rural areas where internet penetration is low, making it crucial for information dissemination and fact-checking. The vast amount of radio content produced daily makes manual monitoring and fact-checking nearly impossible. There was a need for an automated solution to transcribe, archive, and extract claims from radio shows.

As the Head of Innovation at CJID, Monsur Hussaine, explained, they “set out to achieve this [Dubawa] because the radio is the mass media in Africa. That’s where the majority of Africans get their news. Yet there’s no documentation on the radio.”

The sheer number of radio stations, many of which run 24 hours a day, makes it nearly impossible for any human to effectively track and analyse all the content and information being disseminated on the platform. As a result, fact-checkers have traditionally struggled to keep up with misinformation and disinformation spread on radio programs.

CJID decided to improve monitoring efficiency by automation. “That was why we set out to build a tool that’d monitor the information being disseminated on the radio, by transcribing, archiving and extracting claims from the radio shows”, says Hussain.

Ideation: Identifying the Need for Dubawa Audio AI

The ideation process for Dubawa Audio AI began with recognising the documentation gap in radio content, despite its cultural importance in Africa. The team conducted user research and feasibility studies with journalists and fact-checkers to understand the challenges they faced. They identified specific examples, like the Brekete show, where disinformation flourished due to a lack of efficient monitoring and the show’s emotional appeal. This led to the conception of a tool that could automate the processes of monitoring, transcription, and claim identification.

Through this process, the vision for Dubawa Audio AI took shape as a solution that would use artificial intelligence to transform how radio content is monitored and fact-checked across Africa, addressing a critical need in the fight against misinformation.

Building the Tool: Features and Core Functionalities

From the outset, Dubawa Audio AI aimed to address two core functionalities: transcribing radio shows by automatically converting audio into text, which could be archived and referenced later, and claim extraction to identify specific statements from radio broadcasts that require fact-checking.

As the developers worked on improving these core features, they realised that the archival function provided an unexpected benefit. By storing large volumes of transcribed radio content, they had effectively created a radio monitoring tool. The developer noted, “Based on the archival of all these transcriptions, we’ve actually designed a radio monitoring tool, which means by making our data public, anybody can track what’s going on on the radio. One could search for a keyword, and see anywhere it has been mentioned on all of these radio shows, and be able to understand the context in which it was mentioned.”

The radio monitoring feature evolved through several iterations from the initial solution. The team now has an archive of transcribed radio shows, which they are looking to make public to allow people to monitor ongoing broadcasts in real-time.

Development and Implementation

Monsur describes the development process: “This is a tool that people have not built before, and this is the first tool with that niche capability, perhaps anywhere in the world, so there was no template to follow. It just involved a lot of testing and experimenting.”

Initially, they started with the Google transcription tool, which did not work well due to accent and dialect barriers. The issue persisted with other transcription tools, leading the team to partner with a local transcription company. They leveraged hours of radio recordings to manually transcribe shows, building a more accurate dataset.

Through this process, Monsur learned an important lesson: “We are the ones that need to focus on our own problems, especially in catching up with technological advancements in areas of natural language processing (NLP). We need to work on our own languages for the diversification of large language models.”

Technical Challenges

The development of Dubawa Audio AI faced several significant technical challenges, primarily in the areas of language and accent representation, transcription accuracy, and claim extraction.

Language and Accent Representation was one of the primary challenges in achieving accurate transcription, particularly given the diversity of accents and languages in the region. The lead developer noted, “If you look at our accents, even the way I would speak English. And we will mix English with our local languages. A lot of that. So you’d find that there’s a gap really between that and what AI systems understand.” Existing AI models struggled with African languages and accents, and the team had to deal with mixed language use, such as English interspersed with local languages. Different accents within the same language, like Yoruba, posed additional challenges.

Monsur explains their approach to addressing this challenge: “What we are trying to do now is contribute to dataset improvement on African languages representation. Through the Dubawa audio platform, we have an archive of 100s of hours of audio recordings and their transcriptions. That’s a large dataset, and we are looking at expanding that over the next few months. This dataset would be very valuable in contributing to the language support in Africa.” He further notes, “I know there are existing datasets for languages like Yoruba, for example, but they don’t use representative data. The ones I know are Bible TTS and Afro TTS; the bible data is not very representative of the Yoruba Language, and neither is the Afro TTS. There’s a lot of work into understanding how the representation and bias would affect any model being built on such a dataset.”

Transcription Accuracy presented another significant challenge. Initial attempts using Google’s transcription tool were inadequate for the task. To address this, the team partnered with a local transcription company to develop a more accurate model. They also had to differentiate between various language varieties, such as Nigerian English, Ghanaian English, and Nigerian Pidgin, to ensure each model was used specifically for that language.

Claim Extraction was the third major technical hurdle. The team initially used the Distilbert model for claim extraction. However, they later transitioned to OpenAI models after their launch, which significantly improved performance. Monsur explained, “We started building before the launch of OpenAI, and for the claim extraction, we were using Distilbert initially, and then after OpenAI’s launch, we tested out OpenAI models, and we saw that it had better performance. This meant we had to drop months of hard work to move to an Open AI model, and this increased the tool’s accuracy in that regard.”

These technical challenges underscore the complexity of developing AI tools for diverse linguistic environments and the importance of continual adaptation to new technologies and methodologies.

Overcoming Challenges

The team’s approach to overcoming these obstacles involved continuous testing and iteration of different technologies, building a substantial dataset of over 100 hours of audio recordings and transcriptions, and focusing on creating value before considering monetisation.

One of the most formidable hurdles was managing the sheer volume of content. Initially rolled out to monitor three radio stations in Nigeria and one in Ghana, the tool had to sift through hours of radio broadcasts daily. The developers needed to find ways to streamline the claim detection process to make it manageable and effective.

The solution lay in leveraging AI to significantly reduce the workload. Instead of requiring human fact-checkers to manually listen to endless hours of radio, the AI was designed to present only claims that were potentially false or worth investigating. As one of the developers pointed out, “We basically reduced the time needed for our fact-checkers to get to the right information.”

This approach dramatically improved efficiency, allowing the team to process large volumes of content quickly. However, it’s important to note that while the tool provided automated insights, the final decision on whether to fact-check a claim still rested with human journalists. This approach highlighted the importance of maintaining human oversight in AI-powered journalism tools, ensuring that the technology augments rather than replaces human judgment.

By focusing on these strategies — iterative development, building a robust dataset, and prioritizing efficiency and value creation — the Dubawa team was able to overcome the initial implementation challenges and create a tool that effectively addresses the needs of fact-checkers in the African media landscape.

Feedback Loop

Monsur explains that they have gotten substantial feedback on the improvements that need to be made to the user experience “Before we launched the product we conducted internal testing with everyone at the CJID. We saw that a lot of features had to be improved, which helped us build a better product. The idea of radio monitoring came from user feedback as well. It’s a continuous process for us, and we have seen a lot of improvement over time.”

Outcome and Impact

Dubawa Audio has made significant strides in its implementation and adoption across West Africa. The tool currently monitors four radio shows — three in Nigeria and one in Ghana — demonstrating its ability to handle diverse content across different regional contexts.

The impact on the fact-checking process has been substantial. As one of the developers explains, “So rather than having to listen to hours and hours of radio… we basically reduce the time needed for our fact-checkers to get to the right information. It’s still a human decision to decide on what to fact-check. Dubawa AI just sources the claims from the radio shows.” This statement underscores how the tool has streamlined the fact-checking workflow, allowing human fact-checkers to focus their efforts on verifying the most relevant claims rather than sifting through hours of content.

The platform’s user base has grown impressively, now boasting over 150 users. Interestingly, while the tool was initially conceived for fact-checking, many journalists have found it valuable primarily for its transcription capabilities. This unexpected use case highlights the tool’s versatility and its potential to address multiple needs in the journalism ecosystem.

To ensure the tool’s effectiveness, the Dubawa team has implemented a rigorous accuracy benchmarking process. They compare the AI-generated transcriptions with manual transcriptions of the same content, calculating a percentage accuracy score. This ongoing evaluation allows them to continually refine and improve the tool’s performance.

Since its launch, Dubawa Audio AI has had a notable impact on fact-checking efforts, particularly by reducing the time spent reviewing radio broadcasts, allowing fact-checkers to focus on the most relevant information.

The tool’s real-time monitoring capabilities also improved the speed at which misinformation could be addressed, giving newsrooms in Nigeria and Ghana a tool that made fact-checking more efficient.

Overall, Dubawa Audio has demonstrated its value in significantly reducing the time needed for fact-checkers to identify relevant claims, while also providing useful transcription services to a broader journalistic audience. Its success in monitoring multiple radio shows across different countries suggests the potential for further expansion and impact in the fight against misinformation in Africa.

Lessons Learned

The development of Dubawa Audio AI provided valuable insights for AI implementation in journalism, particularly in the African context. These lessons can guide other newsrooms and organisations looking to develop similar tools:

Staying updated with technological advancements proved crucial. The team had to adapt quickly, switching from Distilbert to OpenAI models for better performance. As one team member noted, “The biggest lessons we learned would probably be that we need to stay on top of technological advancements. You can’t just start with one technology and want to stick with that all along.”

The project highlighted Africa’s need for more representation in AI development. The lack of African language and accent representation in existing AI models underscores the importance of developing local solutions.

Collaboration with local partners was key to success. Partnering with a local transcription company proved more effective than using off-the-shelf solutions, especially in handling the nuances of local languages and accents.

User experience emerged as a critical factor. Continuous feedback and redesigns were necessary to improve usability for non-technical users, ensuring the tool’s adoption and effectiveness.

The team learned to consider broader applications beyond the initial scope. While originally conceived as a fact-checking aid, Dubawa Audio AI evolved into a comprehensive radio monitoring platform, demonstrating the value of remaining open to new possibilities.

Long-term challenges, particularly in language representation and accent variation, require ongoing attention and resources. The team recognised that these issues would continue to need focus and innovation.

Adaptability and evolution became central to the project’s success. The team remained open to evolving the tool based on early outcomes, which allowed them to add valuable features like archival monitoring that weren’t part of the original plan.

The project reinforced that AI complements rather than replaces human judgment. While AI significantly reduced the workload, human oversight remained crucial, with fact-checkers making the final decisions on what to fact-check.

Addressing local contexts proved essential. Dubawa Audio AI’s focus on radio, a dominant media source in Africa, underscored the importance of tailoring AI solutions to regional media consumption patterns. This approach highlighted that tools developed for other markets might not be as effective in different contexts.

Finally, the value of an in-house technical team became apparent. CJID’s team of machine learning engineers, data engineers, and software developers was crucial to the project’s success. As one team member explained, “We also have this AI in journalism fellowship, where we put tech guys and journalists or researchers on the same team to ideate and develop products to solve journalism problems. These journalists take the products to their newsrooms to support their work, and it motivates stakeholders in seeing why it’s important to have a technical team in-house.”

Future Outlook

The team behind Dubawa Audio AI has ambitious plans for the tool’s future development and impact. These plans encompass several key areas of focus:

Expanding language support is a primary goal. The team aims to include more African languages like Yoruba, Hausa, and Igbo, broadening the tool’s applicability across diverse linguistic communities in Africa.

Building comprehensive datasets is another crucial objective. The team is working on creating datasets that represent various accents, genders, and dialects. This effort will enhance the tool’s accuracy and effectiveness across different African contexts.

Government collaboration forms an important part of the future strategy. CJID is part of the Nigerian AI Collective and is working with the government, with support from Luminate, to advance the broader AI Ecosystem in Nigeria. This collaboration could significantly accelerate the development of AI solutions in the information ecosystem.

The team is also positioning Dubawa Audio as a key tool for media monitoring during future election cycles. As one team member envisions, “I expect that by the next general election cycle, it’s going to be a key determinant of understanding every information being disseminated on the radio. It’s going to be a one-stop shop for everything media monitoring that relates to radio.” This focus could make Dubawa Audio an essential resource for ensuring informed and fair electoral processes.

Open-source contribution is another avenue the team is exploring. There are plans to contribute to the advancement of AI in African languages, potentially through open-source initiatives. This approach could foster collaboration and accelerate progress in developing AI solutions for African languages.

Conclusion

The development of Dubawa Audio demonstrates the potential for AI solutions tailored to the unique challenges of sub-Saharan African journalism. By focusing on local needs, collaborating with regional partners, and continuously adapting to technological changes, the team has created a valuable tool for fact-checkers and journalists. As AI continues to evolve, projects like Dubawa Audio pave the way for more inclusive and effective technological solutions in the region. Monsur says “foundational problems for Nigeria are infrastructure and computer, and there are different gaps in each newsroom. It all depends on the vision of the leadership in the newsrooms.”

Thank you to Aisha Bello for research and copy support.

First published on Medium By Stephanie 'S.I' Ohumu on January 30, 2025.

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Exported from Medium on April 22, 2025.

There is a place for you in AI. No matter who you are, where you live, or what expertise you have.

You get to decide how you contribute to the development, use, governance, and more, of artificial intelligence. Opportunities abound.

On this fourth episode, Amy Bilton who heads business engagement and enterprises at the University of Hull’s Data Science, AI and Modelling Centre (DAIM) joins hosts S.I Ohumu and Mohineet Kaur to discuss opportunities that exist for all in AI.

Listen to the episode below, and read on for resource links and more on the expert featured in the discussion.

Press play to listen to episode four of You and AI

Listen to episode three of You and AI wherever you listen to podcasts:

Apple Podcasts | Spotify | and more.

Resources:

Academic Research

• National Institute for Health Research. (n.d.). AI Award. Retrieved May 12, 2023, from https://www.nihr.ac.uk/explore-nihr/funding-programmes/ai-award.htm

• The Alan Turing Institute. (n.d.). Research and Funding Calls. Retrieved May 12, 2023, from https://www.turing.ac.uk/work-turing/research-and-funding-calls

Apps and APIs for Business

• Hugging Face. (n.d.). Spaces. Retrieved May 12, 2023, from https://huggingface.co/spaces

• OpenAI. (n.d.). Examples. Retrieved May 12, 2023, from https://platform.openai.com/examples

Scholarships

• Keele University. (n.d.). Office for Students Funded Scholarship for the MSc in Artificial Intelligence and Data Science. Retrieved May 12, 2023, from https://www.keele.ac.uk/study/postgraduatestudy/tuitionfeesandfunding/postgraduatefunding/postgraduatebursariesandscholarshipsuk/officeforstudentsfundedscholarshipforthemscinartificialintelligenceanddatascience/

• Office for Students. (n.d.). £8 million boost for digital student scholarships. Retrieved May 12, 2023, from https://www.officeforstudents.org.uk/news-blog-and-events/press-and-media/8-million-boost-for-digital-student-scholarships/

• DeepMind. (n.d.). Education. Retrieved May 12, 2023, from https://www.deepmind.com/education

• Warwick Global Partnerships. (n.d.). Feuer International Scholarships in AI. Retrieved May 12, 2023, from https://warwick.ac.uk/global/partnerships/mwa/education/education-projects/feuer/scholarships/#:~:text=The%20Feuer%20International%20Scholarships%20in%20AI%20offer%20a%20generous%20funding,%C2%A35%2C000%20for%20four%20years

• Udacity. (n.d.). AWS AI/ML Scholarship Program. Retrieved May 12, 2023, from https://www.udacity.com/scholarships/aws-ai-ml-scholarship-program

• RBC Data Science and Artificial Intelligence. (n.d.). NF3 (Copy). Retrieved May 12, 2023, from https://rbcdsai.iitm.ac.in/nf3-copy/

Online course — Beginner

• Coursera. (n.d.). AI for Everyone. Retrieved May 12, 2023, from https://www.coursera.org/learn/ai-for-everyone

Communities in AI

• Women in AI. (n.d.). Retrieved May 12, 2023, from

https://www.womeninai.co/

• Black in AI. (n.d.). Retrieved May 12, 2023, from

https://blackinai.github.io/#/

Books

• Lee, K. F., & Qiufan, C. (2021). AI 2041.

• Lee, K. F. (2018). AI Superpowers.

• Nadella, S. (2017). Hit Refresh.

About the Expert

Amy Bilton heads Business Engagement and Enterprise at Hull University’s Data Science, AI and Modeling Centre, DAIM.

Amy has over fifteen years of experience in innovation and business engagement across academic and corporate research environments.

She has an undergraduate degree in Biological Sciences, MPhil in Agriculture, MA in creative writing and has worked in various positions like KTP (Knowledge Transfer Partnership) Associate at Newcastle University, Project Manager at the UK Civil Service, from Junior Research Analyst to Associate Manager at Nielsen, Business Development Manager at the University of London as well as Knowledge Transfer Officer at CERN (derived from the acronym for the French “Conseil Européen pour la Recherche Nucléaire”), which is a European Organisation for Nuclear Research.

First published on Medium by Stephanie S.I Ohumu on May 12, 2023.

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Exported from Medium on April 22, 2025.

Artificial Intelligence is not without its challenges.

Some of these include bias and fairness, questions on ethics and privacy, and many more.

Having discussed first the foundations and then usability of AI, on this third episode of You and AI, hosts S.I Ohumu and Mohineet Kaur speak with Dr Marco Ortolani about what ails AI and how one might go about thinking through solutions, if at all possible.

Listen to the episode below, and read on for resource links and more on the expert featured in the discussion.

Press play to listen to episode three of You and AI

Listen to episode three of You and AI wherever you listen to podcasts:

Apple Podcasts | Spotify | and more.

References:

EU AI Act:

• European Commission. (2021). EU Artificial Intelligence Act. Retrieved from

https://artificialintelligenceact.eu/

AI and Job Loss

• Graetz, G., & Michaels, G. (2019). Robots at work: the impact on productivity and jobs. IZA Journal of Labor Policy, 8(1), 1–21. doi: 10.2478/izajolp-2019–0004

AI Ethics

• Turing Institute. (2019). Understanding artificial intelligence ethics and safety. Retrieved from https://www.turing.ac.uk/sites/default/files/2019-06/understanding_artificial_intelligence_ethics_and_safety.pdf

Algorithmic bias

• Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (pp. 77–91). doi: 10.1145/3178876.3186151

• YouTube video. Code.org. (2017, June 27). What is algorithmic bias? [Video]. YouTube.

About the Expert

Dr Marco Ortolani works as a Senior Lecturer at Keele University for the last four years. He has a PhD in Computer Engineering from the University of Palermo (Italy), in which his research focused on the use of fuzzy logic and market basket analysis to categorise genomic and proteomic data. He has been in the field of research for more than 20 years and has published various research papers, journal articles and chapters.

His current research regards the design of smart systems for managing large amounts of data. His focus is in particular on the extraction of meaningful knowledge, by producing human-interpretable models, particularly in structural knowledge extraction and explainable machine learning.

First Published on Medium by Stephanie S.I Ohumu on May 10, 2023.

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Exported from Medium on April 22, 2025.

The world has problems.

Healthcare is abysmal, the climate crisis, mass extinction, people are hungry and incredibly poor, and political instability is rife, as is inequality.

With all of these big, existential problems, most wonder why exactly we are focusing instead on making Artificial Intelligence.

On the second episode of You and AI, hosts S.I. Ohumu and Mohineet Kaur explore truly meaningful use cases for Artificial Intelligence.

Listen to the episode below, and read on for resource links as well as more on the AI usability expert featured in the discussion, Dr Chandra Sekhar.

Press play to listen to episode two of You and AI

Listen to episode two of You and AI wherever you listen to podcasts:

Apple Podcasts | Spotify | and more.

References:

The World’s Biggest Problems

• Wikipedia contributors. (2023, April 25). List of global issues. In Wikipedia, The Free Encyclopedia. Retrieved 02:39, May 3, 2023, from https://en.wikipedia.org/wiki/List_of_global_issues

Healthcare

• World Health Organisation. (2021, January). The top 10 causes of death. Retrieved from https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death

• Imperial College London. (2021). Healthcare AI. Retrieved from https://www.imperial.ac.uk/stories/healthcare-ai/#:~:text=Perceptual%20AI%20replicates%20the%20ability,how%20patients%20should%20be%20treated

• IBM Watson Health. (n.d.). Solutions. Retrieved from https://www.ibm.com/watson-health/solutions/

• Aidoc. (n.d.). Homepage. Retrieved from

https://www.aidoc.com/

Climate Crisis

• WattTime. (n.d.). Homepage. Retrieved from

https://www.watttime.org/

• ClimateAI. (n.d.). Homepage. Retrieved from

https://climate.ai/

Press play to listen to episode two of You and AI

About the Expert

Dr Chandra Sekhar is an expert in deployable and usable AI. He has a PhD in Machine/ Deep Learning from the Indian Institute of Information Technology, SriCity. Presently, he works as a Lead Engineer at Samsung Electronics and as a reviewer for IEEE, Elsevier and Springer Nature journals.

He has worked in the software industry for around 12 years in various roles like Lead, AI ML product consultant and more and has published his work in high-ranking conference venues.

First published on Medium by Stephanie S.I. Ohumu on May 2, 2023.

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Exported from Medium on April 22, 2025.

Press play to listen to episode one of You and AICogito, ergo sum. I think, therefore I am. — Renee Descartes

Our personhood is tethered to our ability to think and our capacity for intelligent behaviour. Language, inference, perception, collaboration, deduction, and emotion are what it means to be human.

It is no surprise that the advancements in Artificial Intelligence raise so many questions. Some are existential, and others concern the use of this new technology, its challenges, and implications.

Your questions about AI deserve answers.

That is why we,  S.I. Ohumu and Mohineet Kaur,  have made a podcast. It’s called You and AI. The goal is to help understand artificial intelligence, how AI affects you, and how you affect it. All delivered in accurate, fun, expert-driven yet easy-to-understand conversations.

Each episode is available to stream wherever you listen to podcasts and is accompanied by a post with relevant links and a brief biography of the expert.

This first episode defines artificial intelligence, traces the history of AI development, and explains some commonly used terms.

Press play to listen to episode one of You and AI

Find You and AI wherever you listen to podcasts:

Apple Podcasts |Spotify | and more.

References:

What is Intelligence?

- Bench-Capon, T. J. M. (1990). Knowledge Representation: An Approach to Artificial Intelligence. Ellis Horwood Series in Artificial Intelligence. Ellis Horwood Limited.

- Wikipedia contributors. (2023, April 27). Intelligence. In Wikipedia. Retrieved May 1, 2023, from https://en.wikipedia.org/wiki/Intelligence

What is Artificial Intelligence?

- Wikipedia contributors. (2023, April 29). Artificial intelligence. In Wikipedia. Retrieved May 1, 2023, from https://en.wikipedia.org/wiki/Artificial_intelligence

Types of AI

- IBM. (n.d.). Strong AI. Retrieved May 1, 2023, from https://www.ibm.com/uk-en/topics/strong-ai#:~:text=Weak%20AI%2C%20also%20known%20as,to%20solve%20for%20new%20problems.

History of AI Development

- Wikipedia contributors. (2023, April 27). Timeline of artificial intelligence. In Wikipedia. Retrieved May 1, 2023, from https://en.wikipedia.org/wiki/Timeline_of_artificial_intelligence

- Harvard Medicine News. (2017, August 4). A brief history of artificial intelligence. Harvard Medical School. Retrieved May 1, 2023, from https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/

- Simonite, T. (2022, February 18). The History of IBM’s Deep Blue, the First Computer to Beat a Chess Grandmaster. MIT Technology Review. Retrieved May 1, 2023, from https://www.technologyreview.com/2022/02/18/1044709/ibm-deep-blue-ai-history/

- BBC Teach. (n.d.). 15 Key Moments in the Story of Artificial Intelligence. BBC. Retrieved May 1, 2023, from https://www.bbc.co.uk/teach/ai-15-key-moments-in-the-story-of-artificial-intelligence/zh77cqt

Definition of Terms

- Wikipedia contributors. (2023, April 14). Deep learning. In Wikipedia. Retrieved May 1, 2023, from https://en.wikipedia.org/wiki/Deep_learning

- MIT Sloan School of Management. (n.d.). Machine Learning Explained. Retrieved May 1, 2023, from https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained

About the Expert

Prisilia Mbuy is a senior IT service management professional. She specialises in Cloud Service Management. She is currently pursuing her PhD from Keele University with a focus on the algorithmic impact assessment to promote the ethical use of Artificial Intelligence

She has an MBA from Lancaster University Management School and an MSc in Information Technology and a BSc in Mathematics.

She has been in the computer science field for 20+ years and has worked with Freshfields Bruckhaus Deringer, Cegedim Rx, Informed Solutions, Sungard Vivista and more.

Currently working as a Senior Consultant at Microsoft, she focuses on assisting clients in transforming and implementing best practices for cloud service management so they can derive business value from their cloud services.

Originally posted by Stephanie S.I Ohumu on May 1, 2023.

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Exported from Medium on April 22, 2025.

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Meet the Creators…

You and AI was created by two friends and classmates while studying for an MSc in Artificial Intelligence and Data Science at the University of Keele, England. They have since graduated.

Meet Stephanie(S.I.).

S.I. Ohumu has received two Emmy nominations for her journalistic work with BBC Africa Eye. An Ofsted scholar, she leverages her technical and storytelling expertise to broaden the understanding of and diversity in AI.

Connect with her on LinkedIn.

And this is Mohineet Kaur.

She has worked as a research assistant in image digitisation and reconstruction using MATLAB and has published papers. Proficient in C++, Arduino, MATLAB, Java, Microsoft Office and more with strong research and analytical abilities, she has an MSc. in Physics. Connect with her on LinkedIn.

First Published by Stephanie 'S.I. ' Ohumu on May 1, 2023.

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Exported from Medium on April 22, 2025.