Science Ethics

"While they are distinct lines of research, the ethics of data, algorithms and practices are obviously intertwined, and this is why it may be preferable to speak in terms of three axes defining a conceptual space within which ethical problems are like points identified by three values" (Floridi & Taddeo, 2016, p. 4)

Data Ethics considers issues surrounding data creation, recording, curation, sharing, and use, as well as the professional and social practices around data. 

Algorithm Ethics considers issues surround the creation of algorithmic systems, usage, impact, and accountability as well as the labor practices around algorithms, both who is doing the labor to train the systems and whose labor the systems are effecting.

Where can I start thinking ethically about data and algorithms?

The University of Michigan, Ann Arbor's Center for Ethics, Society, and Computing is a great place to start, they regularly have events on these issues.

You may also be interested in Critically Conscious Computing, which provides a critical examination of computing foundations and how to teach them, and Applying and Ethics of Care to Internet Research, which discusses how to ethically engage in research on internet data.

I have some data, how do I handle it ethically?

A great place to start would be to make sure that you are handling the data with CARE. Created as a set of principles for data sovereignty of Indigenous data, and imperative to implement for all Indigenous data, they are solid ethical principles to consider when collecting and using any data. They are:

• Collective Benefit - Data ecosystems shall be designed and function in ways that enable Indigenous Peoples to derive benefit from the data.
• Authority to Control - Indigenous Peoples’ rights and interests in Indigenous data must be recognised and their authority to control such data be empowered. Indigenous data governance enables Indigenous Peoples and governing bodies to determine how Indigenous Peoples, as well as Indigenous lands, territories, resources, knowledges and geographical indicators, are represented and identified within data.
• Responsibility - Those working with Indigenous data have a responsibility to share how those data are used to support Indigenous Peoples’ self-determination and collective benefit. Accountability requires meaningful and openly available evidence of these efforts and the benefits accruing to Indigenous Peoples.
• Ethics - Indigenous Peoples’ rights and wellbeing should be the primary concern at all stages of the data life cycle and across the data ecosystem.

There are more in-depth explanations of each of the principles you can find here. Also, remember that while it is imperative you use the CARE principles with any and all Indigenous data, making sure you are prioritizing the wellbeing, rights, and benefits of any people whose data you are holding is a solid ethical move.

It is not enough to simply handle the data with CARE, you should also be FAIR with the data. The FAIR principles were created to make sure data is well managed and reusable. They are:

• Finable - The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.
• Accessible - Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorization.
• Interoperable - The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.
• Reusable - The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.

You may also consider asking yourself the questions from this article and making sure you are meeting the guidelines set out in the US federal government's Data Ethics Framework and the Urban Institute's Principles for Advancing Equitable Data Practice. 

I want to use a generative AI in my work, what are the questions I should be considering?

The MIDAS Guide for using Generative AI for Research is a great resource. It covers everything from citation practices to its trustworthiness to transparency.

I am planning to create an Algorithmic System, how can I approach doing so in the most ethical manner possible?

This is a very active and open area right now. Two places I would suggest starting are the UNESCO Ethics of Artificial Intelligence and NIST's Trustworthy & Responsible AI Resource Center.

There are many resources you will find at both, including: Recommendations on the Ethics of AI, Actionable Policies for implementation of algorithmic systems, a Risk Management Framework, and the Characteristics of a Trustworthy AI System.

You may also be interested in the 6 areas of Ethical Considerations for Machine Learning and this talk by Timnit Gebru, a leading light of the ethical algorithm world, on The Path to Community Centered AI Research.

Floridi Luciano and Taddeo Mariarosaria 2016. What is data ethics? Phil. Trans. R. Soc. A.3742016036020160360
http://doi.org/10.1098/rsta.2016.0360