Responsible AI in Generative AI

Challenges, Strategies, and Emerging Regulations This session will explore responsible AI approaches for defining and managing Generative AI use cases, focusing on balancing organizational efficiency with system suitability. Key topics include aligning GenAI strategies with Responsible AI principles, addressing emerging regulatory requirements, and overcoming challenges in system risk management.

Hear exclusive insights and success stories with Kevin Chung CSO at Writer

Delve into exclusive insight from Kevin Chung, Chief Strategy Officer at Writer, as he shares his journey and experiences at Writer. Gain an insider’s perspective on AI's role in the future of work, balancing innovation and risk, driving adoption, the challenges and opportunities ahead, and how organizations can prepare for an AI-driven future.

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In this conversation, we hear from Anuj Bindal, the General Manager and Director of Visual Shopping at Amazon, as he shares insights into Amazon's journey with Generative AI.

Interview with Khalil Maaouni, Head of Data and Digital at Coca-Cola Bottlers Japan.

There is a worldwide wave of excitement around the revolutionary potential of generative AI.

On October 16th-19th 2023, that global buzz will be concentrated in Atlanta, Georgia, where experts from around the world will congregate for Generative AI Week, with topics under discussion including data leverage and balancing capability and risk.

One of Generative AI Week’s expert speakers – Khalil Maaouni, head of data and digital at Coca-Cola Bottlers Japan – is at the cutting edge of both those topics. He believes that one of generative AI’s key attributes is its potential to “dynamize access to data”. This involves making data “a lot more available faster” as well as “getting a pulse on what is being looked at in the company in terms of the context of data”.

According to Maaouni, this ability to explore the context of data means generative AI can fill important gaps in companies’ current capabilities.

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Interview with Daniel Hulme, Chief AI Officer at WPP. 

Organizations spanning a wide range of sectors are beginning to recognize the boundless opportunities and uses for generative AI in gaining a competitive edge. These possibilities are set to be explored in detail at Generative AI Week, taking place in Atlanta, Georgia, on October 16th-19th 2023.

Among the world’s foremost AI experts speaking at the event will be Daniel Hulme, Chief AI Officer at WPP. Hulme has a quarter of a century’s worth of experience in AI within academia. He is also entrepreneur in residence at University College London and has acted as an expert witness to the UK all-party parliamentary group on artificial intelligence. So when he talks about generative AI, it makes sense to listen. And significantly, he’s never been more excited about the power and potential of the technology than right now.

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While most enterprise businesses are running Generative AI pilot projects, typically they are taking a cautious approach to customer facing applications due to the reputational, financial and brand implications should something go wrong.

Booking.com are an example of a business that have already rolled out a customer facing product with Generative AI at its core, the AI Trip Planner. The planner incorporates the benefits of LLM’s with the value of their internal data, to deliver a differentiated product in the travel market.

In this interview with Charlotte Munro, Global Product Marketing – Generative AI at Booking.com she covers the business case behind the product, how they build the internal team that worked on it, the risks they faced and how they managed them, and the early feedback from their customers.

Why did Booking.com decide to integrate Generative AI into its products?

• While the AI Trip Planner is a new product that we developed and launched in July of this year, AI and ML has always been an integral part of Booking.com, recommending destinations and options to millions of travellers every day on the platform. We have been using AI extensively for years in order to optimise interactions with our customers who are both travellers and partners - from laser focussing / contextualising / tailoring recommendations and the user experience, through analysing the content and pictures provided by our partners and customers. It is the strong foundations of AI within Booking.com’s ecosystem that allowed us to develop a robust AI Trip Planner so quickly.
• We believed then, and we continue to believe now, that we can build a more compelling and differentiated offering if we can leverage AI technology to deliver a more tailored and relevant booking experience, a connected trip, that would be more responsive to a booker's needs and help manage the different aspects/components of their trips.
• While we make some use of OpenAI’s ChatGPT to partially power the new conversational experience, Booking.com’s existing machine learning models enrich the information presented to customers with tailored destinations and accommodation options.

How did you assemble the team to create your first customer facing Generative AI products? What job functions were represented?

• We assembled a virtual task force team of colleagues that represented all functions of our business. This included our ML experts, Product, UX and UI as well as our research, product marketing, legal and PR teams.
• To compliment the technical skills and requirements, and ensure we created a product that resonates seamlessly with our customers, we then focused on ensuring we had the right customer insights, legal protections, and impactful key messaging. This meant we not only had a great MVP product, but also one which should have longevity build in
  

How are you ensuring that customers get a differentiated experience when using Generative AI?

• We know Booking.com is really good at helping our users book their trip, especially when they know where they want to go and have a fair idea of when they want to travel. However, we know that before travellers get to that point there is a lot of discovery and inspiration that happens. The AITP allows users to start their search and discovery based on their trip intent - whether it be a beach holiday with their family or a hiking adventure with their friends or simply exploring a range of potential destinations and options. This means we are interacting with customers throughout their entire trip journey – from ideation through to bookings.
• The AITP allows for a more unconstrained search experience where users can quite simply tell us what they want, and we show them options that suit their needs. Travellers can ask the AI Trip Planner general travel-related questions, as well as more specific queries to support any stage of their trip planning process. This includes scoping out potential destinations and accommodation options, providing travel inspiration based on the individual traveller’s needs and requirements, as well as creating itineraries for a particular city, country or region.
• The AITP also really helps travellers by saving them time and effort - doing the hard work for them.
• We are always looking for new ways to make customer interaction smoother and richer and our recommendations have a seamless visual UX which really helps our travellers explore and get an idea of a potential destination or accommodation for their next trip.

How did you manage the risks associated with the technology, such as privacy and security?

• Our customers' rights, privacy and security is always of the utmost importance to us, which is why we engaged our legal experts from day one of the build. The AI Trip Planner went through a robust product review process and has been developed according to rigorous standards to ensure a safe and inclusive environment for travel exploration. We used the same robust legal protections that we use with all of our products and this includes custom AI moderation layers for the AITP, which detect and block harmful content, and ensure our customer’s data remains safe and compliant with all regulations.
• We also carry out robust data protection impact assessments to identify any areas of potential risk and to ensure compliance with the applicable data protection laws
• With all our travel experiences we want to ensure that travel is a safe and inclusive environment for everyone, and this traveller experience carries through to how customers interact with the AI Trip Planner.

How do you protect personal information and prevent hallucinations?

• Booking.com has built a custom moderation layer to structure the collaboration between the LLM and Booking.com’s machine learning models, as well as guardrails to detect and remove certain personal information and/or non-travel related content. This also works together with OpenAI’s moderation to help identify and block potentially harmful content. The aim is to remove personal data when it’s unnecessary to the search and travel planning process.
• To minimise and mitigate any inappropriate and inaccurate responses being surfaced to travellers, we have introduced moderation content filters which remove any offensive questions and informs the user very clearly that the tool cannot and will not respond to such questions.
• We have made some good strides in moderating and filtering out inappropriate content and will continue to innovate and develop within this space to optimise further and incorporate learnings to ensure accuracy and relevancy of the content that is surfaced. We have also carried out robust data protection impact assessments to identify any areas of potential risk and to ensure compliance with the applicable data protection laws.

How do the LLM and Booking.com work together to answer a question?

• The AI Trip Planner is a combination of third-party LLM technology and Booking.com’s own machine learning recommendation systems. The LLM technology that we are currently leveraging from OpenAI enables the AI Trip Planner to quickly read and understand questions that are being posed in the tool, as well as to provide responses in a conversational, natural way. The LLM also references a wealth of other data to provide certain kinds of recommendations, for example itineraries with suggestions of great things to do in a specific destination.
• Where the Booking.com machine learning models take over are when the data needed to respond to the question is fully in Booking.com’s realm of expertise, for example with specific destination and property recommendations. All of the visual elements in the tool, including pricing information are from Booking.com.
• To make it even more concrete/simple, conversational text and answers to general travel questions are provided by ChatGPT. For example, what’s the weather like in Bali in December? Or what’s a good itinerary to visit the north of Italy for a week? The visual links for specific destinations and property recommendations, as well as the moderation layers that help keep content travel-related and safe come from Booking.com.

Do you have any initial market feedback on how the product has been received?

• The objective of rolling out the MVP slowly and market by market allows us to continue to develop and optimise the product based on how our customers are interacting with the product and what they are using it for/type of broad queries that they are asking. We are seeing travellers explore popular destinations as well as themes like beaches and family trips. Itineraries are also popular. What would you do differently if you ran the project again?

What do you wish you had known before developing this product?

• I think the nature of AI and this type of emerging technology means that you are never going to have the perfect test and launch conditions. Which is why developing an MVP that we gradually roll out to our customers and new markets, allows us to improve the product and the experience. As we can get early signals from our customers - how are they interacting with the product, what kind of themes they are exploring and how are they using it? We will continue to evolve the AITP and ensure that the product meets the needs of our customers.
  

The media crescendo surrounding generative AI has reached such a pitch in recent months that it’s difficult to ascertain genuine insight from all the noise. This means it’s more important than ever to go directly to the experts working at the cutting edge to discover the true significance of the latest developments.

Well, the expert opinion is in: believe the hype, because “there’s a big change coming”.

That’s the view of Chris Booth, generative and conversational AI consultant and product owner for NatWest Group’s artificially intelligent agent: Cora.

So where are we now with generative AI, and where are we heading? When assessing the potential impact of generative AI and the disruption that could be coming down the line, Booth says it’s helpful to think in terms of where we are on the ‘sigma curve’.

“What I mean by that is, if we’re at the top, then most of the impact has already happened and we won’t see much change going forward,” he explains. “If we’re in the middle of the curve, then we can still expect to see generative AI applied to other technologies in the future. Or are we at the start of the sigma curve, with big changes to come?

“Overall,” he says, “that’s where I am – I think there’s a big change coming.”

Booth is one of the experts speaking at the Generative AI Summit, taking place at Hilton Syon Park, London, on 16th and 17th May 2023. He’ll be addressing the topic of what generative AI means for chatbots, drawing on his direct experience of working with NatWest’s Cora chatbot.

Powered by IBM Watson, Cora operates in the closed domain, which is where chatbots primarily exist – especially in large organisations – responding to action- or task-oriented questions such as, ‘Can you change my address?’

“It’s basically a large logic tree,” Booth explains. “This means we dictate what buttons are presented and then what button you click obviously changes your path. So that’s closed domain, and it works really well.”

However, closed domain and logic trees can have limitations, says Booth. “Multiple trees are brittle. They can become very difficult to manage and maintain as they grow at an exponential rate. And the larger the tree, the more links you have to manage and it’s a mess.”

But this is where generative AI has the potential to change things massively, Booth insists. While he admits that generative AI is “nothing new” in the natural language processing (NLP) space – and that it’s only in the last few years that models have become good enough to make generative AI “a contender” – Booth is excited by the potential for changes it could bring for the “opposite end” – the open domain.

He explains: “The open domain deals with questions like, ‘What did Obama do before he was president?’ – an open-ended question that can be difficult to answer. And that’s where logic trees really struggle to capture the potential scope and possibilities of how you can answer that question.

“So that’s where generative AI has huge potential for expansion, with the potential of opening new use cases for businesses to approach.”

However, there are challenges and possible drawbacks. Among them are transparency and explainability.

“Generative AI is usually powered by language models – deep AI machine learning,” Booth explains. “And these deep neural networks have billions and billions of parameters, which makes it difficult to distinguish and understand how the AI has come to its decisioning.”

Also, the language models can be prone to ‘hallucinations’ – which Booth describes as “a fancy word for outputting nonsensical and incorrect answers”. From a language model perspective, these can be very difficult to control, he says. And added to these issues are obstacles surrounding cost, privacy and data security.

But despite the challenges, Booth believes everyone will be putting generative AI to practical use at some point. “There’s going to be varying degrees of how quickly it happens. There are already plenty of startups based on ChatGPT and GPT-3. And there are small businesses in marketing, for example, that are going absolutely nuts with ways of slowly automating things.”

What’s more, opportunity is ripe for breakthroughs in the development of generative AI. “There’s the potential to make a massive impact,” Booth insists. And he’s hoping to realise that potential himself. He reveals: “I’ve got a project right now I can’t talk about in detail, but we’re trying to find ways to cover the gaps and weaknesses of language models. If we do, the implications are pretty large.”

Find out more about the big changes on the way at the Generative AI Summit.