Enriching human knowledge with responsible AI
Responsible AI Principles
Elsevier provides information-based analytics and decision tools for researchers and health professionals worldwide, helping them advance science and improve healthcare outcomes, for the benefit of society.
For more than a decade, Elsevier has been using AI and machine learning technologies responsibly in our products combined with our unparalleled peer-reviewed content, extensive data sets, and sophisticated analytics to help researchers, clinicians and educators discover, advance and apply trusted knowledge.
Our responsible AI principles
We evaluate the real-world impact of our solutions on people.
We take action to prevent the creation or reinforcement of unfair bias.
We support transparency and can explain how our solutions work.
We promote accountability through meaningful human oversight.
We respect privacy, protect intellectual property, and champion robust data governance.
Responsible artificial intelligence principles at RELX
Our solutions, both internal and external, enhance human decision-making. This approach is underpinned by our commitment to corporate responsibility, which we define as the way we do business, proactively working to increase our positive impact and prevent negative impact. We are using ever more sophisticated analytics and technology as we deliver higher value-add solutions. Our products and applications span a continuum of information-based analytics and decision assistive tools from providing insights, to identifying potential risk factors and recommendations, to assisting customers and colleagues in their decision-making processes. The intersection of advanced technology, its increasing closeness to decision-making, and its possible societal effects pose new risks but also offer numerous chances for societal benefits.
Purpose and scope of the responsible AI principles
Generally, we use the term Artificial Intelligence (AI) to describe machine-based systems, including generative AI systems, which infer solutions to set tasks and have a degree of autonomy. The scope of our RELX 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 provide high level guidance for anyone at RELX working on designing, developing, and deploying machine-driven insights.
They provide a risk-based framework drawing on best practice from within our company and other organizations. Individual business areas own the practical implementation of the principles. RELX and its businesses have established robust enterprise-wide policies and processes that are applicable to AI-enabled solutions. The purpose of the Responsible AI Principles is to complement these. AI is a field that evolves continually, at unprecedented speed and scale. These Principles will iterate over time, based on colleague and customer feedback, as well as industry and legislative trends. This will allow us to be proactive, ensuring our solutions develop in line with our values, and maintaining our stance as a thought leader in the market.
1. We evaluate the real-world impact of our solutions on people
This evaluation helps us to build trustworthy solutions and business practices, guided by our company values, including RELX’s commitment to benefiting society through its solutions and operations.
Recognising that our solutions assist our customers in their decision-making, we are conscious of the potential impacts they have on people and society.
We are committed to responsible innovation and recognise that context and underlying assumptions shape the design choices of our solutions and business practices. We therefore consider the sphere of influence of each solution or practice, including who may be impacted by them and how. This includes looking beyond direct customers to the wider contexts in which our solutions and business practices operate, such as potential effects on people’s health, livelihood, or rights.
We gather both quantitative and qualitative feedback to understand how AI is used over time, and to inform our direction. These insights enable us to evaluate and respond to the human impact of AI in our solutions and business practices. As AI technologies evolve, we continue to understand and manage their impact; this includes adopting collective standards and working with partners who share these principles.
2. We take action to prevent the creation or reinforcement of unfair bias
This drives high-quality results and averts discrimination.
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, and therefore less valuable. It also may lead to less favorable 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 implementation of procedures, extensive review and documentation processes of our models and products, such as the use of available automated bias detection tools.
We support transparency and can explain how our solutions work
An appropriate level of transparency for each application and use case enables users and stakeholders to understand and trust our solutions and their outputs.
Transparency helps build trust with users and regulators. We focus on ensuring transparency about the AI models and products we develop, and on providing meaningful explanations of system outputs where appropriate. We use a variety of models and do not prohibit the use of closed models.
As part of the design process, we determine what aspects of a solution require explanation, to whom, and how. Explanations are consistent but tailored in depth and technical detail so that users can understand, appropriately rely on, and trust its use. We also assess a solution’s reliability and are clear about its intended use and limitations.
4. We promote accountability through meaningful human oversight
This enables ongoing, robust quality assurance of machine outputs and helps pre-empt unintended use.
Our technology enhances our customers’ and employees’ decision-making processes. We ensure human accountability over the development, use, and outcomes of AI systems. This includes clear assignment of oversight responsibilities, effective review and understanding, capacity to intervene where needed, evaluation of performance under realistic conditions, and ongoing monitoring and response after deployment. This approach is central to ensuring the quality and appropriate performance of our solutions.
When our solutions are used by customers, they remain responsible as the ultimate decision-maker and must use our solutions in line with agreed terms and 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 our teams.
5. We respect privacy, protect intellectual property, and champion robust data governance
This ensures we continue to be recognized as a trusted provider of information solutions.
Responsible collection, use and protection of data and content are crucial to our long-term success as an information and analytics business. Governance, robust data security and respect for personal data and intellectual property (IP), underpin the trust our customers, partners and data providers continue to place in us. As we maintain and broaden our data assets and develop new content and insights, strong data governance remains essential. AI systems perform more accurately when informed by high-quality data, and the use and reuse of data must be governed by appropriate IP rights, contractual permissions, and defined purposes. We therefore maintain robust data management practices, including data minimisation and retention, quality assessment, IP review, and security policies and procedures.
Some data sets include personal information. We are committed to acting as responsible stewards of personal information under our Privacy Principles and handling such information in accordance with all applicable privacy laws and regulations.
We respect and protect IP rights throughout the lifecycle of our products, technologies, and AI systems, not only informed by applicable law and contractual obligations, but also through established protections for proprietary information.