How should firms navigate data ethics?

We often see news articles on how the most recent cyber-attack has exposed huge swathes of sensitive data, compromising user accounts, bank details and passwords. However, we shouldn’t only think about how companies are protecting data but also the way they collect and use it.

The opinions expressed here are those of the authors. They do not necessarily reflect the views or positions of UK Finance or its members.

Data ethics denotes the moral principles and guidelines that govern the collection, handling, and use of data. With data generation expected to reach 180 zettabytes by 2025, up from 64.2 in 2020, it has become more important than ever to consider the ethical implications of how and why data is used. [1]

The key problems with modern data usage

Data capture and storage has evolved in parallel with the digital revolution. With the proliferation of apps and websites, data is captured effortlessly, often without due consideration.

Key problems include:

Recording everything, because the data might be useful in the future. This approach is contrary to the data minimisation principle: data should only be stored if it’s necessary for something. [2]

Nobody really knows how the data will be used in the future; there is a risk that people’s data will be used in ways that they would never expect. [2]

Cambridge Analytica provides an example of a firm harvesting the personal data of millions of Facebook users without their consent, aiming to influence critical elections through targeted political adverts. This highlights how the application of the data had a greater implication than the simple action of harvesting it. [3]

How must data ethics evolve with AI?

In a 2019 US study, it was discovered that an algorithm employed by hospitals to allocate healthcare unfairly discriminated against black individuals. The algorithm assessed risk scores based on patients' healthcare costs, resulting in a disadvantage for black patients who spent less on healthcare compared to similar white patients. Only 17.7 per cent of black patients identified as needing additional care according to the algorithm, while an unbiased algorithm would have identified 46.5 per cent of them. [4]

This highlights a growing epidemic of black box algorithms in our models today, where users and developers cannot easily decipher the inner working of the algorithms. To the extent that the complexity of such models’ ‘box’ out stakeholders entirely from understanding how the algorithm has come to decisions, leading to unintended consequences such as discrimination. [5, 6]

How should companies be tackling data ethics?

Not only are the ethical implications of these practices of concern to the people impacted by them, but they can also affect the firm’s brand image, reputation and credibility. Not to mention the potential for regulatory consequences.

Organisational first steps

Setting expectations around data usage, developing a culture of adherence to data ethics principles and having a clear identity and access-management system help ensure only those with the right training and privileges can access sensitive data.

Organisations should also develop a data ethics governance committee, composed of representatives across the business to define and uphold data standards.

Technical first steps

The growing emphasis on comprehending algorithms is backed by Explainable Artificial Intelligence (XAI), a field which aims to improve the explainability of AI models while maintaining performance. It achieves this by adding an extra layer to the algorithm solely for understanding the factors that influence decision-making or outcomes. Adopting XAI can assist firms in proactively preventing and identifying biases before they have real-world consequences. [7]

Transparency is key in tackling data discrimination, privacy, and security issues. Firms should know, at a minimum, what information is gathered, for what purposes, who has access to it, and what security controls are in place. The validity of black box algorithms needs regular checking.

The benefits accruing to organisations and consumers through data-driven capabilities will continue to rise. When data ethics align with a firm’s data activities, this enhances its capabilities as opposed to restricting them.

UK Finance has published several thought pieces on data and AI ethics:

Area of expertise:

Payments, innovation & resilience

Notes to editor

Works Cited

[1]	P. Taylor, “Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2020, with forecasts from 2021 to 2025,” Statistica, 8 September 2022. [Online]. Available: https://www.statista.com/statistics/871513/worldwide-data-created/.
[2]	D. J. Hand, “Aspects of Data Ethics in a Changing World: Where Are We Now?.,” in Big Data, 2018, pp. 176-190.
[3]	Carole Cadwalladr, Emma Graham-Harrison, “The Guardian,” The Guardian, 17 March 2018. [Online]. Available: https://www.theguardian.com/news/2018/mar/17/cambridge-analytica-facebook-influence-us-election.
[4]	Z. Obermeyer, “Science,” in Science, 2019, pp. 447-453.
[5]	J. Ayling and A. Chapman, “Putting AI ethics to work: are the tools fit for purpose?,” AI Ethics 2, p. 405–429, 2022.
[6]	A. Jobin, M. Ienca and E. Vayena, “The global landscape of AI ethics guidelines,” Nature Machine Intelligence, vol. 1, p. 389–399, 2019.
[7]	Surkov, Alexey; Gregorle, Jill; Srinlvas, Val, “Unleashing the power of machine learning models in banking through explainable artificial intelligence (XAI),” 2022.