Considering Ethics in a Data Driven World

To my mind, data management; understanding the quality, architecture and strategy, needs to be the first consideration when working with data. So, what part does ethical thinking play in that?

Data ethics relates to good practice around how data is collected, used and shared. We need to consider the ethical implications in data handling on people and society, either directly or indirectly. Handle data with practices that reflect the values of society. Data ethics should therefore be considered at all stages of data handling from stewardship, creating analysis, products and services, and deciding what to do next.

The ethics of data handling are complex, but center about 3 core concepts:

Impact on people: Because data represents characteristics of individuals and is used to make decisions that affect people’s lives, there is an imperative to manage its quality and reliability and integrity.

Potential for misuse: Misusing data can negatively affect people and organizations. It is imperative therefore to prevent both internal and external misuse.

Economic value: Data can have significant economic value both for producers and consumers, yielding unprecedented insights and meaningful economic benefit. Ethics of data ownership should determine how and what value can be accessed and by whom, to an ethical and fair benefit.

Technological innovations have increased the sheer volume of data available and created greater distance between human judgement and automated decision-making. This is well exampled in the global battle to become a dominant force in AI, both as a consumer and as a technology developer. Commercialising this technology at scale is the next big rush. AI thrives on data, where huge volumes of content are needed to train algorithms. We all know any application of AI will only be as good as the volume and quality of data ingested. But much of that data is collected directly from individuals over ordinary activities likes e-commerce purchases or simply walking down the street (in certain jurisdictions). Some of the primary ethical issues involved in data management around AI therefore is privacy, security and use of personal data.

A point to stress, is the difference between compliance and ethics. Essentially different sides of the same coin, compliance is reactive, and means adhering to rules and requirements as outlined by the law. Ethics is about doing what is right, and being proactive, rather than reactive. But because humans are diverse - different genders, races, values and cultural norms – we can see very quickly, one ethical standard will not suit all.

China is renowned for surveillance via facial recognition. The sheer number of cameras installed has quickly built the largest monitoring system on the planet. A striking example of this is Alibaba’s Hotel of The Future. Connected to the Chinese identity system, guests self-manage reservations and payments via app, and enter rooms using facial-recognition technology, eradicating the need to provide keycards. From 2020, China progresses with a compulsory national ranking scheme, rewarding and punishing citizens base on their social credit score. Comparatively, places like San Francisco and Okland in California have already made the legal decision to ban facial recognition for surveillance within city limits. Interestingly, the utilitarian argument can be argued for in both examples.

So, as the data environment rapidly evolves, organisations need to recognise and respond to their ethical obligations. But there is a real-world line between the soft behavioural management of ethical principles; and the hard data management requirements needed for legal and regulatory compliance. Whilst legislation cannot have real-time updates covering all risks, compliance requirements are based in ethical practices. Laws and policies do codify ethical principles. And we see this in the world around us, being driven by our politicians, law makers and leaders. For example, because she believed it was the ethically correct thing to do, Angela Merkel defined her open immigration policy on Germany’s borders despite significant opposition. Or Judy Smith (the real-life Olivia Pope), CEO, founder and president of a crisis management firm who also drives public policy work in housing crises and education, because it’s a cause close to her. In the world of data and closer to home, Elizabeth Denham, the Information Comissioner, is charged with protecting UK’s data. Denham is responsible for ensuring corporations and political parties are transparent in their use of personal information and has personally committed to increasing consumer trust in personal data.

Repeatedly, we can see ethical drivers are key motivators for society’s rules, laws, and practices. And given that ethical data handling at its core is considering the impact on people and society, in a world of rapid data innovation, it’s clear we need representative teams in strategy, delivery and management across the industry if we are to give data ethics the fair considerations it deserves.

Attend our Women in Data Masterclass on 28th November to find out more on how a modern data team should innovate, collaborate and deliver.

by Gina Sharma

Posted on November 20, 2019