Responsible AI: 5 principles practiced at LexisNexis

Legal technology is here and it's here to stay. 

In fact, the legal industry reported more awareness than the general public of the increasing power of tools like ChatGPT—the powerful generative AI tool that sparked a global revolution across many industries. A LexisNexis survey completed in the US in March 2023 showed 86% of American lawyers reporting awareness of Gen AI (as compared to 57% of the public) and 51% planning to use Gen AI tools or are currently using them.

But how is LexisNexis walking the talk with responsible artificial intelligence (AI)?

Responsible AI principles at RELX

Generally, the company uses AI to describe machine-based systems that infer solutions to set tasks and have a degree of autonomy. The scope of RELX's responsible AI principles, however, is broader than AI and includes any machine-driven insights resulting from the tools and techniques within the field of data science. 

These principles guide anyone at RELX working on designing, developing, and deploying machine-driven insights. They also provide a risk-based framework drawing on best practices from within our company and other organisations — individual business areas on the practical implementation of the principles.

Beyond this, RELX and its business already have robust policies and processes in place for AI-enabled solutions. The Responsible AI principles complement these. Plus, these principles will iterate over time based on colleague and customer feedback, as well as industry and legislative trends. 

A five part approach

As part of RELX's responsible approach, we: 

• Consider the real-world impact of our solutions on people 
• Take action to prevent the creation or reinforcement of unfair bias
• Explain how our solutions work 
• Create accountability through human oversight 
• Respect privacy and champion robust data governance

#1: We consider the real-world impact of our solutions on people

Recognising that our solutions may assist our customers in their decision-making, we are mindful of the potential impacts our solutions may have on people. 

AI is a method to solve a business problem for our customers as well as our own company, implying a set of assumptions and a specific, real-world context. The better that context is understood and the more aware we are of our assumptions, the better the solutions we create, and the higher the value-add for customers. 

We go beyond asking "What are we building, and who is the customer?" We seek to identify the range of people who benefit from our solution and how, and who might be impacted and why. 

To do so, we define the sphere of influence of the solution. We map stakeholders beyond direct customers, and we think about the domain to which the solution applies – are people's health, livelihood (including career prospects) or rights affected in any way? These insights enable us to consider the impact of a particular solution.

#2: We take action to prevent the creation or reinforcement of unfair bias

As a supporter of the United Nations Global Compact, promoting fairness and non-discrimination is at the core of our business philosophy and values. 

We understand that mathematical accuracy doesn't guarantee freedom from bias, which is why we act to prevent the creation or reinforcement of unfair bias. 

When such actions are not taken, bias can be introduced inadvertently via data inputs and/or through machine processing or algorithms. Once introduced, it can be replicated through human decision-making across data science, product management, and technology. 

That can lead to results that are skewed. It also may lead to less favourable outcomes for individuals or groups based on gender, ethnicity, socio-economic status, and other personal attributes. 

Our actions to prevent the creation or reinforcement of unfair bias include the implementation of procedures, extensive review and documentation processes, and the use of available automated bias detection tools, among others.

#3: We can explain how our solutions work

This principle doesn't prescribe what models to build and use, and it doesn't prohibit the use of "closed box" models. The aim is to have an appropriate level of transparency for each application and use case to ensure that different users can understand and trust the output. 

Different contexts and audiences require different explanations. As part of the design process, we consider what elements of the solution will need explaining, to whom they might need to be explained, and how to go about explaining them. We also evaluate a solution's reliability and are explicit about its intended use.

#4: We create accountability through human oversight

Our technology assists our customers' decision-making processes. It is important that humans have ownership and accountability over the development, use, and outcomes of AI systems. 

We have an appropriate level of human oversight throughout the lifecycle of our solutions. This is core to ensuring the quality and appropriate performance of our solutions. 

Once the solution has left our hands, this means the customer takes on the role of ultimate decision-maker. Use of our solutions is controlled by an agreed set of terms and conditions, as well as applicable law. We hold our customers accountable to these requirements. Customer support colleagues play an important role in ensuring the intended use is understood by customers and quality issues are dealt with appropriately by internal teams. 

#5: We respect privacy and champion robust data governance

Appropriate collection, usage and protection of data are crucial to our long-term success as an information and analytics business.

As we maintain and broaden our data assets and discover new ways of generating insights, we recognise that strong data governance is essential. AI systems function more accurately when they are fed large amounts of high-quality data, and some data sets are utilised across solutions, for multiple purposes. We need to ensure we have in place robust data management and security policies and procedures.

Some data sets include personal information. We are committed to handling personal information in accordance with all applicable privacy laws and regulations as well as our own Privacy Principles, which require that we always act as responsible stewards of personal information. 

**

At LexisNexis, our purpose is to advance the rule of law. This purpose guides our actions and so when developing solutions, we ensure that all of our developments are in line with RELX's responsible AI principles. These principles ensure that our solutions develop in line with our values and maintain our stance as a thought leader in the market.