AI Across Industries and the Importance of Responsible AI

Oracle University Podcast

AI is reshaping industries at a rapid pace, but as its influence grows, so do the ethical concerns that come with it. This episode examines how AI is being applied across sectors such as healthcare, finance, and retail, while also exploring the crucial issue of ensuring that these technologies align with human values. In this conversation, Lois Houston and Nikita Abraham are joined by Hemant Gahankari, Senior Principal OCI Instructor, who emphasizes the importance of fairness, inclusivity, transparency, and accountability in AI systems. AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ---------------------------------------------------- Episode Transcript:

00:00

Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started!

00:25

Lois: Welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services.

Nikita: Hey everyone! In our last episode, we spoke about how Oracle integrates AI capabilities into its Fusion Applications to enhance business workflows, and we focused on Predictive, Generative, and Agentic AI.

Lois: Today, we’ll discuss the various applications of AI. This is the final episode in our AI series, and before we close, we’ll also touch upon ethical and responsible AI.

01:01

Nikita: Taking us through all of this is Senior Principal OCI Instructor Hemant Gahankari. Hi Hemant! AI is pretty much everywhere today. So, can you explain how it is being used in industries like retail, hospitality, health care, and so on?

Hemant: AI isn't just for sci-fi movies anymore. It's helping doctors spot diseases earlier and even discover new drugs faster. Imagine an AI that can look at an X-ray and say, hey, there is something sketchy here before a human even notices. Wild, right?

Banks and fintech companies are all over AI. Fraud detection. AI has got it covered. Those robo advisors managing your investments? That's AI too.

Ever noticed how e-commerce companies always seem to know what you want? That's AI studying your habits and nudging you towards that next purchase or binge watch.

Factories are getting smarter. AI predicts when machines will fail so they can fix them before everything grinds to a halt. Less downtime, more efficiency. Everyone wins.

Farming has gone high tech. Drones and AI analyze crops, optimize water use, and even help with harvesting.

Self-driving cars get all the hype, but even your everyday GPS uses AI to dodge traffic jams. And if AI can save me from sitting in bumper-to-bumper traffic, I'm all for it.

02:40

Nikita: Agreed! Thanks for that overview, but let’s get into specific scenarios within each industry.

Hemant: Let us take a scenario in the retail industry-- a retail clothing line with dozens of brick-and-mortar stores. Maintaining proper inventory levels in stores and regional warehouses is critical for retailers. In this low-margin business, being out of a popular product is especially challenging during sales and promotions. Managers want to delight shoppers and increase sales but without overbuying. That's where AI steps in.

The retailer has multiple information sources, ranging from point-of-sale terminals to warehouse inventory systems. This data can be used to train a forecasting model that can make predictions, such as demand increase due to a holiday or planned marketing promotion, and determine the time required to acquire and distribute the extra inventory.

Most ERP-based forecasting systems can produce sophisticated reports. A generative AI report writer goes further, creating custom plain-language summaries of these reports tailored for each store, instructing managers about how to maximize sales of well-stocked items while mitigating possible shortages.

04:11

Lois: Ok. How is AI being used in the hospitality sector, Hemant?

Hemant: Let us take an example of a hotel chain that depends on positive ratings on social media and review websites. One common challenge they face is keeping track of online reviews, leading to missed opportunities to engage unhappy customers complaining on social media. Hotel managers don't know what's being said fast enough to address problems in real-time. Here, AI can be used to create a large data set from the tens of thousands of previously published online reviews.

A textual language AI system can perform a sentiment analysis across the data to determine a baseline that can be periodically re-evaluated to spot trends. Data scientists could also build a model that correlates these textual messages and their sentiments against specific hotel locations and other factors, such as weather. Generative AI can extract valuable suggestions and insights from both positive and negative comments.

05:27

Nikita: That’s great. And what about Financial Services? I know banks use AI quite often to detect fraud.

Hemant: Unfortunately, fraud can creep into any part of a bank's retail operations. Fraud can happen with online transactions, from a phone or browser, and offsite ATMs too. Without trust, banks won't have customers or shareholders. Excessive fraud and delays in detecting it can violate financial industry regulations.

Fraud detection combines AI technologies, such as computer vision to interpret scanned documents, document verification to authenticate IDs like driver's licenses, and machine learning to analyze patterns. These tools work together to assess the risk of fraud in each transaction within seconds. When the system detects a high risk, it triggers automated responses, such as placing holds on withdrawals or requesting additional identification from customers, to prevent fraudulent activity and protect both the business and its client.

06:42

Nikita: Wow, interesting. And how is AI being used in the health industry, especially when it comes to improving patient care?

Hemant: Medical appointments can be frustrating for everyone involved—patients, receptionists, nurses, and physicians. There are many time-consuming steps, including scheduling, checking in, interactions with the doctors, checking out, and follow-ups. AI can fix this problem through electronic health records to analyze lab results, paper forms, scans, and structured data, summarizing insights for doctors with the latest research and patient history. This helps practice reduced costs, boost earnings, and deliver faster, more personalized care.

07:32

Lois: Let’s take a look at one more industry. How is manufacturing using AI?

Hemant: A factory that makes metal parts and other products use both visual inspections and electronic means to monitor product quality. A part that fails to meet the requirements may be reworked or repurposed, or it may need to be scrapped. The factory seeks to maximize profits and throughput by shipping as much good material as possible, while minimizing waste by detecting and handling defects early.

The way AI can help here is with the quality assurance process, which creates X-ray images. This data can be interpreted by computer vision, which can learn to identify cracks and other weak spots, after being trained on a large data set. In addition, problematic or ambiguous data can be highlighted for human inspectors.

08:36

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09:20

Nikita: Welcome back! AI can be used effectively to automate a variety of tasks to improve productivity, efficiency, cost savings. But I’m sure AI has its constraints too, right? Can you talk about what happens if AI isn’t able to echo human ethics?

Hemant: AI can fail due to lack of ethics.

AI can spot patterns, not make moral calls. It doesn't feel guilt, understand context, or take responsibility. That is still up to us.

Decisions are only as good as the data behind them. For example, health care AI underdiagnosing women because research data was mostly male. Artificial narrow intelligence tends to automate discrimination at scale. Recruiting AI downgraded resumes just because it had a word "women's" (for example, women's chess club).

Who is responsible when AI fails? For example, if a self-driving car hits someone, we cannot blame the car. Then who owns the failure? The programmer? The CEO? Can we really trust corporations or governments having programmed the use of AI not to be evil correctly? So, it's clear that AI needs oversight to function smoothly.

10:48

Lois: So, Hemant, how can we design AI in ways that respect and reflect human values?

Hemant: Think of ethics like a tree. It needs all parts working together. Roots represent intent. That is our values and principles. The trunk stands for safeguards, our systems, and structures. And the branches are the outcomes we aim for. If the roots are shallow, the tree falls. If the trunk is weak, damage seeps through. The health of roots and trunk shapes the strength of our ethical outcomes.

Fairness means nothing without ethical intent behind it. For example, a bank promotes its loan algorithm as fair. But it uses zip codes in decision-making, effectively penalizing people based on race. That's not fairness. That's harm disguised as data.

Inclusivity depends on the intent sustainability. Inclusive design isn't just a check box. It needs a long-term commitment. For example, controllers for gamers with disabilities are only possible because of sustained R&D and intentional design choices. Without investment in inclusion, accessibility is left behind.

Transparency depends on the safeguard robustness. Transparency is only useful if the system is secure and resilient. For example, a medical AI may be explainable, but if it is vulnerable to hacking, transparency won't matter.

Accountability depends on the safeguard privacy and traceability. You can't hold people accountable if there is no trail to follow. For example, after a fatal self-driving car crash, deleted system logs meant no one could be held responsible. Without auditability, accountability collapses.

So remember, outcomes are what we see, but they rely on intent to guide priorities and safeguards to support execution. That's why humans must have a final say. AI has no grasp of ethics, but we do.

13:16

Nikita: So, what you’re saying is ethical intent and robust AI safeguards need to go hand in hand if we are to truly leverage AI we can trust.

Hemant: When it comes to AI, preventing harm is a must. Take self-driving cars, for example. Keeping pedestrians safe is absolutely critical, which means the technology has to be rock solid and reliable. At the same time, fairness and inclusivity can't be overlooked. If an AI system used for hiring learns from biased past data, say, mostly male candidates being hired, it can end up repeating those biases, shutting out qualified candidates unfairly.

Transparency and accountability go hand in hand. Imagine a loan rejection if the AI's decision isn't clear or explainable. It becomes impossible for someone to challenge or understand why they were turned down.

And of course, robustness supports fairness too. Loan approval systems need strong security to prevent attacks that could manipulate decisions and undermine trust.

We must build AI that reflects human values and has safeguards. This makes sure that AI is fair, inclusive, transparent, and accountable.

14:44

Lois: Before we wrap, can you talk about why AI can fail? Let’s continue with your analogy of the tree. Can you explain how AI failures occur and how we can address them?

Hemant: Root elements like do not harm and sustainability are fundamental to ethical AI development. When these roots fail, the consequences can be serious. For example, a clear failure of do not harm is AI-powered surveillance tools misused by authoritarian regimes. This happens because there were no ethical constraints guiding how the technology was deployed. The solution is clear-- implement strong ethical use policies and conduct human rights impact assessment to prevent such misuse.

On the sustainability front, training AI models can consume massive amount of energy. This failure occurs because environmental costs are not considered. To fix this, organizations are adopting carbon-aware computing practices to minimize AI's environmental footprint. By addressing these root failures, we can ensure AI is developed and used responsibly with respect for human rights and the planet.

An example of a robustness failure can be a chatbot hallucinating nonexistent legal precedence used in court filings. This could be due to training on unverified internet data and no fact-checking layer. This can be fixed by grounding in authoritative databases.

An example of a privacy failure can be AI facial recognition database created without user consent. The reason being no consent was taken for data collection. This can be fixed by adopting privacy-preserving techniques.

An example of a fairness failure can be generated images of CEOs as white men and nurses as women, minorities. The reason being training on imbalanced internet images reflecting societal stereotypes. And the fix is to use diverse set of images.

17:18

Lois: I think this would be incomplete if we don’t talk about inclusivity, transparency, and accountability failures. How can they be addressed, Hemant?

Hemant: An example of an inclusivity failure can be a voice assistant not understanding accents. The reason being training data lacked diversity. And the fix is to use inclusive data.

An example of a transparency and accountability failure can be teachers could not challenge AI-generated performance scores due to opaque calculations. The reason being no explainability tools are used. The fix being high-impact AI needs human review pathways and explainability built in.

18:04

Lois: Thank you, Hemant, for a fantastic conversation. We got some great insights into responsible and ethical AI.

Nikita: Thank you, Hemant! If you’re interested in learning more about the topics we discussed today, head over to mylearn.oracle.com and search for the AI for You course. Until next time, this is Nikita Abraham….

Lois: And Lois Houston, signing off!

18:26

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