A short introduction to accountability in machine-learning algorithms under the GDPR

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 by Andreia Oliveira, Master in EU Law (UMINHO)
 and Fernando Silva, Consulting coordinator - Portuguese Data  Protection National Commission

Artificial Intelligence (AI) can be defined as computer systems designed to solve a wide range of activities, that are “normally considered to require knowledge, perception, reasoning, learning, understanding and similar cognitive abilities” [1]. Having intelligent machines capable of imitating human’s actions, performances and activities seems to be the most common illustration about AI. One needs to recognise AI as being convoluted – thus, machine learning, big data and other terms as automatization must hold a seat when discussing AI.  Machine learning, for example, is defined as the ability of computer systems to improve their performance without explicitly programmed instructions: a system will be able to learn independently without human intervention [2]. To do this, machine learning develops new algorithms, different from the ones that were previously programmed, and includes them as new inputs it has acquired during the previous interactions.

The capabilities of machine learning may put privacy and data protection in jeopardy. Therefore, ascertaining liability would be inevitable and would imply the consideration of inter alia all plausible actors that can be called upon account.

Under the General Data Protection Regulation (GDPR), the principle of accountability is intrinsically linked to the principle of transparency. Transparency empowers data subjects to hold data controllers and processors accountable and to exercise control over their personal data. Accountability requires transparency of processing operations, however transparency does not constitute accountability [3]. On the contrary, transparency acts as an accountability’ helper – e.g. helping to avoid barriers, such as opacity.
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