Category: Ethics

Mapping ethico-legal principles for the use of Artificial Intelligence in gastroenterology

Stewart C, Wong SKY, Sung JJY.

J Gastroenterol Hepatol. 2021. DOI: 10.1111/jgh.15521.

Ethical decision box
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This article outlines a number of core ethical and legal principles to consider when examining the use of AI in gastroenterology and laying the foundation for an ethical framework that could be employed in the resolution of difficult choices concerning the use of AI in clinical practice. 

  1. AI systems must function in a robust, secure, and safe way throughout their life cycles, and potential risks should be continually assessed and managed.
  2. Respect for people is the primary value that underpins many of the values. Respect for people is illustrated by a moral attitude of deference and consideration of the interests of others.
  3. In real-life clinical practices, there will not be a single correct decision but a number of ethically viable alternatives.

Machine Learning and Natural Language Processing in mental health: systematic review

Le Glaz A, Haralambous Y, (...), Lemey C.

J Med Internet Res. 2021. DOI: 10.2196/15708.

Geographical distribution of authors.
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Machine Learning and NLP techniques provide useful information from unexplored data (eg, patients’ daily habits that are usually inaccessible to care providers). Before considering it as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner.

  1. Ethical issues, such as predicting psychiatric episodes or implications in the physician-patient relationship, should be discussed in a timely manner.
  2. ML and NLP methods may offer multiple perspectives in mental health research, and they should be considered as a tool to support clinical practice.
  3. Language in both spoken and written forms plays an important role in Machine Learning (ML) mental health applications. It is therefore essential to understand what natural language processing (NLP) is before discussing the joint applications of ML and NLP in mental health.

Ethical machines: the human-centric use of Artificial Intelligence

Lepri B, Oliver N, Pentland A.

iScience. 2021. DOI: 10.1016/j.isci.2021.102249.

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This publication focuses the attention on existing risks related to the use of algorithmic decision-making processes, including computational violations of privacy, power and information assymetry, lack of transparency and accountability, and discrimination and bias. 

  1. Only with multidisciplinary approaches we can achieve algorithmic fairness.
  2. It’s very important to have an ethical and human-centric use of Artificial Intelligence.
  3. We must help people to understand AI-driven decision making processes.

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