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We invite you to submit papers for this workshop. We are living through a data revolution, which will be as transformative of our society as the industrial revolution. Algorithms, and in particular, learning algorithms, are the engines of this revolution. ‘Intelligent’ algorithmic systems impact many areas of our personal and professional lives, making decisions based on prior ‘learned’ knowledge. The use of learning algorithms and has the potential to revolutionise scientific discoveries. However, these discoveries have the potential to be simultaneously beneficial and detrimental at the same time if they are not undertaken in a responsible and ethical manner. A few of these Major themes for this workshop are detailed below, although all other contributions surrounding the use of AI in Scientific Discovery are welcomed!
- Data Decision Making in Drug Discovery & Healthcare - Which would you trust more, a human or machine designed drug? Would you rather let a human or a machine make a decision about your healthcare? Different tasks align better with human or machine intelligence, but how far should we be allowing AI to make decisions for us? Where should humans be involved and where should there always be a human sign off, or conversely are there areas where machines should be left in charge to remove the chance of human error?
- Removing Bias in AI in Drug Discovery & Healthcare – Intelligent technologies can be vastly useful in drug discovery and healthcare research as machine learning algorithms can be applied to vast linked datasets to make predictions that humans could not. However, this research may not apply to certain minority groups depending on the data used in the system. This may not be as a consequence of intentional algorithmic/data bias but equally is something that should have been addressed by ethical discussions at the start of a project, as “excluding minorities from healthcare research limits the ability to appropriately care for these population and skews the scientific understanding of disease” and indeed drugs to fight and cure these diseases.
- Responsible AI for Chemicals and Materials Discovery – Molecular compounds and materials underpin just about every aspect of our lives, from sustainable energy to healthcare. Society’s demands for enhanced performance is far outweighing our capability to discover materials that deliver it, so it is unsurprising that researchers are looking at using artificial intelligence and machine learning technologies to explore this space and speed up the discovery of new chemicals. However, do the algorithms to discover these chemicals and materials take into account whether they are environmentally friendly? Or whether certain chemicals could be psychoactive or extremely explosive? Whether materials could be used for ill intent? Does this mean chemicals/materials like this shouldn’t be investigated? Are these methods transparent and explainable?
We favour interdisciplinary papers that consider the sociotechnical aspects of these issues, and we expect submissions to demonstrate evidence that they address genuine problems in the area of AI for Scientific Discovery. Papers that detail original research will be favoured, but equally white papers, position papers and works in progress will also be considered.
Relevant types of submission include (but are not limited to):
- New Methodological Approaches for AI for Scientific Discovery (within the general context of AI for Good)
- Proposals, Solutions or Methodologies for Data Decision Making in Drug Discovery or Healthcare
- Technological Proposals or Solutions to preventing algorithmic bias in Drug Discovery or Healthcare
- Ethical data collection and data usage processes for Scientific Discovery Algorithms
- Ethical Frameworks or Codes of Ethics for AI for Scientific Discovery
- Responsible AI Approaches in Scientific Discovery
- For the Greater Good? Weighing up the benefits and dangers of certain Chemical and Materials discovery
- Detailed Case Studies on Data Ethics and Algorithmic Accountability in AI for Scientific Discovery
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