In this episode of the podcast, members of the InfoQ editorial staff and friends of InfoQ will be discussing the current trends in the domain of AI, ML and Data Engineering.

One of the regular features of InfoQ are the trends reports, which each focus on a different aspect of software development. These reports provide the InfoQ readers and listeners with a high-level overview of the topics to pay attention to this year.

InfoQ AI, ML and Data Engineering editorial team met with external guests to discuss the trends in AI and ML areas, and what to watch out for the next 12 months. This podcast is a recording of this discussion where panelists discuss how the innovative AI technologies are disrupting the industry.

Read a transcript of this podcast panel: https://bit.ly/3LXMcUk 
Find the written report and trends graph on InfoQ: https://bit.ly/3Z8Z5CF 

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Transcript

AI, ML, and Data Engineering Trends in 2024

Welcome back, friends! 🎉 You’re tuned into "AI with Shaily," a podcast hosted by Shailendra Kumar, affectionately known as Shaily. He’s your guide to exploring the captivating realm of artificial intelligence, breaking down complex AI topics into understandable insights that affect our daily lives and critical industries. 🤖✨

In this episode, Shaily dives into the crucial topic of explainable AI (XAI), especially relevant in high-stakes fields like healthcare, finance, and criminal justice. Imagine a hospital relying on AI to decide patient treatments, but neither doctors nor patients understand how the AI reached its conclusions—this opacity poses real risks. That’s why XAI is gaining momentum, aiming to make AI decisions transparent, fair, and trustworthy by 2025 and beyond. 🏥💡⚖️

Shaily highlights several key techniques that help demystify AI’s “black box” nature:

- **Decision Trees and Rule-Based Models:** These are like visual maps showing the logical steps AI takes, making it easier for judges, doctors, or anyone involved to follow the reasoning. Think of it as AI “showing its work,” similar to solving a math problem. 🌳📊

- **Feature Importance Analysis:** This method reveals which input factors most influence the AI’s decision—like uncovering the secret ingredients in your coffee. It’s invaluable for spotting biases or mistakes in AI models. ☕🔍

- **Counterfactual Explanations:** One of Shaily’s favorites! These provide “what if” scenarios, such as “If your income was $1,000 higher, your loan would be approved.” This approach makes AI decisions more relatable and fair by showing how changes affect outcomes. 🔄💰

- **Model Simplification and Rule Extraction:** For complex AI models like deep learning, these techniques simplify the inner workings without losing accuracy, turning mysterious black boxes into understandable models. 🧠🔧

Why is explainable AI so important? Beyond meeting regulatory requirements, XAI builds trust and empowers people to question decisions that impact their lives—whether it’s a denied insurance claim or parole evaluation. Studies show that integrating XAI can improve decision quality by up to 85%, a transformative impact! 📈🤝

Looking ahead, Shaily shares a thought-provoking insight from Gartner: by 2025, half of AI models might still lack sufficient explainability. This means there’s an urgent need for scalable, domain-specific XAI methods that balance accuracy with clarity. ⏳🚀

He also offers a practical tip: whenever you encounter AI-driven decisions—in apps, workplaces, or elsewhere—ask, “Can I get a clear explanation of how this decision was made?” This simple question encourages accountability and pushes for greater transparency. 🗣️✅

Reflecting on his own AI journey, Shaily recalls how early experiences with opaque AI felt like magic or mystery. Thanks to explainable AI, that mystery is now becoming a meaningful conversation—turning powerful technology into something understandable and fair. ✨🔍

He leaves listeners inspired with a memorable quote: “Transparency is the new trust currency in AI.” 💬💎

Stay curious and connected! Follow Shailendra Kumar on YouTube, Twitter, LinkedIn, and Medium for more AI insights. Don’t forget to subscribe to "AI with Shaily" and share your thoughts in the comments—Shaily loves engaging with his audience! 🎥🐦💼✍️

Thanks for tuning in! Until next time, keep questioning, keep learning, and remember: AI should be explainable because decisions that matter deserve the clearest answers. 🌟🤖🗝️

Explainable AI: Unmasking the Black Box to Build Trust and Fairness in 2025

Welcome to *AI with Shaily* 🎙️, hosted by Shailendra Kumar, your trusted guide into the world of artificial intelligence! Today’s episode focuses on a crucial and timely topic: Ethics in data use 🌐⚖️, which is becoming a cornerstone of AI development and deployment, especially looking ahead to 2026.

Shailendra shares a personal story from his experience working on an AI project designed to create fairer job applicant screenings. Despite good intentions, the data fed into the model unintentionally perpetuated biases against certain groups. This moment was a powerful lesson that data science is not just about numbers and algorithms — it’s about responsibility, fairness, and respecting human dignity 🙌.

The conversation then shifts to the current landscape, where there’s a growing emphasis on ethical data practices. Key themes include transparency, bias mitigation, privacy, governance, and social responsibility. Organizations are actively working to reduce bias by enhancing data diversity, deploying fairness-detecting tools, and building AI models that treat everyone equally across critical sectors like hiring, finance, and healthcare 🏥💼💰.

One exciting development is Explainable AI (XAI) 🕵️‍♂️🤖, which aims to make AI decisions understandable to users and regulators alike. This transparency fosters trust, especially when AI impacts important life decisions.

Privacy concerns are front and center as well, with new global regulations prompting data scientists to adopt innovative privacy-preserving methods. Techniques like federated learning allow AI models to be trained without sharing raw personal data, while differential privacy adds subtle “noise” to data to protect individual identities — a clever balance of innovation and responsibility 🔒🔍.

Companies are taking ethics seriously by establishing AI ethics committees, conducting thorough audits, and creating formal guidelines to ensure accountability and alignment with ethical principles. Beyond compliance, there’s a broader push toward social responsibility, making sure AI contributes positively to society without reinforcing bias or mistrust 🌍❤️.

Augmented analytics and synthetic data generation are also highlighted as tools that help glean powerful insights while safeguarding sensitive information, further supporting ethical AI development.

The big takeaway? By 2026, ethics will be an integral part of data science’s DNA — not an afterthought. Balancing innovation with fairness, transparency, privacy, and governance is the future of AI. Shailendra encourages all AI practitioners to keep this ethical mindset front and center, reminding us to always ask, “How explainable and fair is this model?” — a simple yet vital question for responsible AI development ✅.

To close, Shailendra shares a resonant quote from Apple CEO Tim Cook: “Technology must be infused with human values.” This perfectly captures the spirit of ethical AI.

Don’t forget to follow Shailendra Kumar on YouTube, Twitter, LinkedIn, and Medium for more insightful AI content. He invites listeners to share their thoughts and stories about ethical AI in the comments, fostering a community of curious and conscientious AI enthusiasts 🤝💬.

Subscribe now to *AI with Shaily* to stay ahead in the ever-evolving world of artificial intelligence. Until next time, stay curious and stay ethical! 🌟🤖✨

Ethics in AI: How Fairness and Transparency Will Shape Data Science by 2026

What happens when AI stops guessing, - and starts reasoning? In this episode, we dive into the groundbreaking shift from brittle automation to fluid intelligence, exploring how Model Synthesis Architecture (MSA) and mini-world models are redefining what AI can do. Join us as we unpack this hybrid approach that merges neural networks with symbolic logic, bringing adaptability, transparency, and human-like decision-making into real-world applications. From supply chains to healthcare, discover how the next generation of AI isn’t just smarter, - it’s more trustworthy.

Bit & Being - From Brittle to Brilliant: How Model Synthesis Architecture Is Reinventing AI Reasoning

Unlock the secrets of data mastery with Shailendra, a 27-year veteran in AI, ML, and Data Science, driving AU$ 6 Billion in transformative value. As the author of 'Making Money out of Data,' their strategies redefine digital success. With expertise honed at IBM and Accenture, they drive innovation and data monetization. Explore how loyalty programs like Flybuys and Everyday Rewards revolutionize business—connect with Shaily today.

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New tracks tagged #Explainable AI