AI4Good: How to use AI for Good: The Ethical and Societal Implications of using AI in Scientific Discovery. 17th floor of the Easy Village building at Northeastern University Boston, MA, United States, June 30-July 3, 2019 |
Conference website | https://sites.google.com/site/ai3sdusingaiforgood/ |
Submission link | https://easychair.org/conferences/?conf=ai4good |
Submission deadline | April 10, 2019 |
This year the AI3SD Network+ (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) will be running a workshop at the WebSci19 Conference in Boston. Artificial and Augmented Intelligence systems have the potential to make a real difference in the scientific discovery domain however this brings a new wealth of ethical and societal implications to consider with regards to this research (e.g. human enhancement, algorithmic biases, risk of detriment). This workshop looks to explore the ethical and societal issues centered around using intelligent technologies (Artificial Intelligence, Augmented Intelligence, Machine Learning, and in general Semantic Web Knowledge Technologies) to further scientific discovery, with a strong consideration of data ethics and algorithmic accountability. Advances in technology and software are rarely inherently bad in themselves, however that unfortunately does not preclude them from being subverted to ill intent by others; furthermore, as demonstrated by the examples above, even an unintentional lack of care towards ethical codes and algorithmic accountability can lead to societal and ethical implications of scientific discovery. It is our responsibility as researchers to consider these issues in our research; are we conducting studies ethically? What ethical codes can we put in place for scientific discovery research to mitigate against ethical and societal issues. These are really important issues, and they require an interdisciplinary focus between scientists, social scientists and technical experts in order to be comprehensively addressed.
Submission Guidelines
We invite you to submit short papers for this workshop. 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
The papers will receive two reviews based on the originality of the research, relevance to the workshop themes, and quality of presentation.
Papers should be between 4-6 pages long using ACM's double-column format and must adhere to the ACM SIG Conference Proceedings Standards. Templates can be found here: https://www.acm.org/publications/proceedings-template
List of Topics
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?
Programme Committee
- Dr Samantha Kanza (University of Southampton, Network+ Coordinator of AI3SD)
- Dr Nicola Knight (University of Southampton, Chief Admin Officer of AI3SD)
- Professor Jeremy Frey (University of Southampton, Principal Investigator of AI3SD)
Publication
AI4Good proceedings will be published in the ACM WebSci19 Proceedings.
Papers should be between 4-6 pages long using ACM's double-column format and must adhere to the ACM SIG Conference Proceedings Standards. Templates can be found here: https://www.acm.org/publications/proceedings-template
Key Dates
Calls for Papers Opens: 26th February 2019
Paper Submission: 1st April 2019 10th April 2019
Notification of Acceptance: 16th April 2019
Camera Ready Papers: 1st May 2019
Workshop At WebSci19: 30th June 2019
All deadlines are at Midnight Hawaii Time.
Venue
This workshop will take place at the WebSci19 Conference in Boston on the 17th floor of the Easy Village building at Northeastern University, Boston, MA, US
Contact
Any queries should be emailed to Dr Samantha Kanza on s.kanza@soton.ac.uk