Many organisations around the world make one key mistake: thinking that ethical data considerations don’t apply to their business. There are many examples where firms have failed to comply with ethics and had to face the consequences, as one bank discovered three years ago.
In December 2020, the Financial Conduct Authority (FCA) fined Barclays £26 million as the bank had failed to properly contact and have appropriate conversations with consumer credit customers who fell into arrears, instead offering them unaffordable payment plans. This put customers under pressure, negatively impacting their ability to pay mortgages, taxes, child support and utilities. Though the organisation had identified issues previously, systems and controls problems meant the issues went unrectified, ultimately failing to treat customers fairly or act with proper care and diligence.
This story raises vital questions about ‘Data Ethics’, a concept for which the UK only set up its first framework in 2020. This (The Data Ethics Framework) guides appropriate and responsible data use in government and the wider public sector.
Worldwide, there is immense pressure to start regulating the use of AI. In November 2023, the UK’s AI Safety Summit at Bletchley Park took further steps as British Prime Minister Rishi Sunak pushed for the UK to lead the world in AI safety.
At this Summit, all countries present signed a declaration to emphasise the risks posed by powerful AI systems, underscored the responsibility of those creating AI to ensure their systems are safe, and committed to international cooperation to mitigate those risks.
Another such landmark development occurred in December 2023, when negotiators for the European Parliament and Council reached a provisional agreement on the Artificial Intelligence Act to ensure that fundamental rights, democracy, the rule of law, and environmental sustainability will be protected from high-risk AI systems, while boosting innovation and making Europe a leader in the field. The rules establish obligations for AI based on its potential risks and level of impact.
Understanding the wider impact of Data Ethics will be vital for companies going forward to safeguard their business and stay compliant with tighter regulations.
What is Data Ethics?
The ethical use of data is an area of increased focus for organisations and is complementary to similar concepts around Responsible AI. While Responsible AI deals with the middle part of the data supply chain (pre-processing to sharing), Data Ethics deals with best practices across the entire data life cycle.
What are the drivers of Data Ethics?
The main drivers for Data Ethics are regulatory requirements and reputation risk, meaning fair and trustworthy outcomes.
Privacy, ethical considerations, fairness, and products and service outcomes are essential whenever companies use data (including artificial intelligence and machine-learning applications); however for some executives, they often aren’t top of mind.
Why should businesses care?
The concept of Data Ethics places a lot of pressure on firms to rectify their processes and business data models, better understand their customer data, and adjust their processes accordingly.
Automated processes can lead to the exact described issue that the organisation encountered, where customers’ specific circumstances were not considered. The reputational damage, as well as the possibility of financial penalties, should be a serious lesson for businesses to take note of, along with organisations across all other sectors.
Misconceptions about Data Ethics
1. Data Ethics is part of the Legal and Compliance teams' responsibility.
Companies may believe that Legal and Compliance teams are responsible for ethics while Data Ethics is everyone’s domain; regulations do drive ethical standards (such as GDPR, DPA, Data Ethics Principles set in 2020 by the UK Government, Bletchley Declaration by Countries Attending the AI Safety Summit, 1-2 November 2023, and so on) however it is vital to create a culture of ‘responsible data’ usage where every single employee in the organisation play an active role across the organisation, embrace ethics principles and ethical issues surrounding data are respected. Ethics values shall be communicated both internally and externally.
2. We can't focus on Data Ethics due to short-term ROI
To achieve short-term returns on investments, management might be tempted to make unethical data choices without considering that long-term investments in security, data protection, and fair data models will bring higher returns in the long run and improve customer trust.
3. We can look at data from the end of the data life cycle chain, without checking the sources.
Companies may believe ethics should be applied to data by an algorithm, forgetting the importance of third parties’ data and how data is overall modelled, sourced and transformed across the data life cycle. Data Ethics should be applied across the entire data life cycle, and business and data models will need to embed and consider ethics consistently.
What are the challenges of implementing Data Ethics?
1. Data limitations
Some unconscious bias could be inadvertently created; however, validating product/service outcomes might be challenging if data such as gender or race are not collected. Organisations must balance the information they want to gather to maximise customer privacy but avoid unconscious bias.
2. Interconnected systems
Systems can be interconnected and complex; therefore, it can take time to validate outcomes. If processes are automated, it is necessary to keep a level of expertise and know-how to explain why a decision is made. It can also be difficult to understand the holistic view of what third parties are doing in the sense of architecture (i.e., data, security, and entitlements).
3.Skills
People who use and process data need a robust ethical education as they are the ones who will drive fair data and model outcomes. Education and training on Data Ethics are necessary to mitigate the risks associated with bias and misuse of data.
4. External security
Security is a crucial aspect of data protection; if data is not secure, there are risks to damage outcomes and customers.
How should you proceed?
More and more, customers demand organisations to provide fair and trustworthy outcomes and judge organisations on their fairness in doing business. Therefore, maintaining public trust is critical, and organisations should avoid scandals by securing customer data incorrectly, stopping the resale of customer information by data brokers, and unfair bias of specific algorithms.
This pressure – from regulators and customers alike – to increase transparency around how data is collected and used and how algorithms make decisions, means that Business Data Models must be readily explainable, as must rules around the usage of AI.
Organisations should include Data Ethics in their strategy and translate ethical principles into designing products and services. Top management should drive ethical standards and set Data Ethics rules to cascade down the principles across the organisation.
The strategic and commercial objectives should align with customers’ expectations and, obviously, with regulatory and legal requirements for data protection and usage.
How can we help you?
At NTT DATA, we leverage our knowledge and support leaders to embed a Data Ethics Framework that reflects a shared vision and mission for the company’s use of data. We can help you to identify KPIs that can be used to monitor and measure the Data Ethics performance in realising your Data Ethics objectives.
We have in-house data capabilities spanning from data governance and management to infrastructure, data architecture and data security, all to support you with implementing data capabilities based on solid trust and ethical foundations. We can help you to train and increase Data Ethics awareness across your organisation by facilitating formal training programs on Data Ethics.
If you’d like to see how we can help your organisation understand and implement Data Ethics, book a free consultation today.