Beginner’s guide to key AI terms and concepts

This is a short guide to key terms and concepts. You can read a longer paper written by us for the European Rights Academy about the way in which AI and Machine Learning can be discriminatory here.

Artificial Intelligence

In broad terms, Artificial Intelligence or AI is a form of technology, the aim of which is to create computer based systems which are able to mimic human intelligence. A detailed paper exploring the meaning of AI has been produced by the European Commission and is available here.

Algorithm

At the heart of AI is the “algorithm”. Algorithms are a set of steps created by programmers. They usually perform repetitive and tedious tasks in lieu of human actors. For example, when LinkedIn informs a user that someone within her network is also connected to five people who are her contacts, it is an algorithm – and not a human – that has quickly compared the two networks to find common contacts.

Algorithmic decision systems (ADS)

Algorithms are increasingly involved in systems used to support decision making; these are sometimes known as ‘ADS’ (algorithmic decision systems)

Direct discrimination

Direct discrimination is prohibited by the Equality Act 2010. It will occur when someone is treated less favourably because of a protected characteristic.

Importantly, if a rule or provision is applied which means that everyone who is disadvantaged by it shares a particular protected characteristic, and everyone who is not disadvantaged by that rule or provision does not possess the protected characteristic, then direct discrimination will have occurred. A detailed exposition of these type of “proxy” direct discrimination claims is available here.

Other than age discrimination, direct discrimination can never be justified and will always be unlawful unless an exception contained within the Equality Act 2010 applies.

The concept of direct discrimination within the Equality Act 2010 is broad enough to cover “discrimination by association” and “perceived discrimination”. In neither case does the claimant need to actually possess the protected characteristic.

Ethics-by-design

The design and development of AI systems in accordance with an ethical system from the outset.

Equality Act 2010

Key piece of legislation in Great Britain which prohibits many forms of discrimination. A full copy is available here.

Harassment

Harassment is prohibited by the Equality Act 2010. It will occur when a person (A) engages in unwanted conduct related to a relevant protected characteristic, and the conduct has the purpose or effect of violating B’s dignity, or creating an intimidating, hostile, degrading, humiliating or offensive environment for B.

Human-centric approach to AI

To create human-centric AI is to ensure that human values are at its core, such as, the principle of non-discrimination and respect for fundamental rights.

Indirect discrimination

Indirect discrimination is prohibited by the Equality Act 2010. It will occur where a person (A) applies to another person (B) a provision, criterion or practice which is applies or would apply to everyone, but it puts or would put persons with whom B shares a protected characteristics at a particular disadvantage when compared with persons with whom B does not share it, and B is at this disadvantage and A cannot show it to be a proportionate means of achieving a legitimate aim.

Machine learning

The power of an algorithm is often linked to “machine learning” which is a means of refining algorithms and making them more “intelligent”.

Here is an extract from “The privacy pro’s guide to explainability in machine learning” published by the International Association of Privacy Professionals, which explains more:

What is machine learning?
Machine learning is a technique that allows algorithms to extract correlations from data with minimal supervision. The goals of machine learning can be quite varied, but they often involve trying to maximize the accuracy of an algorithm’s prediction. In machine learning parlance, a particular algorithm is often called a “model,” and these models take data as input and output a particular prediction. For example, the input data could be a customer’s shopping history and the output could be products that customer is likely to buy in the future. The model makes accurate predictions by attempting to change its internal parameters — the various ways it combines the input data — to maximize its predictive accuracy. These models may have relatively few parameters, or they may have millions that interact in complex, unanticipated ways. As computing power has increased over the last few decades, data scientists have discovered new ways to quickly train these models. As a result, the number — and power — of complex models with thousands or millions of parameters has vastly increased. These types of models are becoming easier to use, even for non-data scientists, and as a result, they might be coming to an organization near you.

https://iapp.org/news/a/the-privacy-pros-guide-to-explainability-in-machine-learning/

Protected characteristics

These are human characteristics which are protected under the Equality Act 2010 such as it is unlawful to discriminate in relation to them. They include:

  • Gender;
  • Age;
  • Race;
  • Religion or belief;
  • Disability; and
  • Sexual orientation.

Reasonable adjustments

The Equality Act 2010 imposes an obligation upon employers, service providers and public authorities to make reasonable adjustments.

This means that where a provision, criterion or practice of A’s puts a disabled person at a substantial disadvantage in comparison with persons who are not disabled, A must take such steps as it is reasonable to have to take to avoid the disadvantage.

Sexual Harassment

Sexual harassment is prohibited by the Equality Act 2010. It will usually occur when a person (A) engages in unwanted conduct of a sexual nature and the conduct has the purpose or effect of violating B’s dignity, or creating an intimidating, hostile, degrading, humiliating or offensive environment for B.