CVPR 2022 Ethics Guidelines
As Computer Vision research and applications have increasing real-world impact, the likelihood of meaningful social benefit increases, as does the attendant risk of harm. The research community should consider not only the potential benefits but also the potential negative societal impacts of CV research, and adopt measures that enable positive trajectories to unfold while mitigating risk of harm. We strongly encourage authors to discuss such ethical and societal consequences of their work in their papers in a concrete manner, while avoiding excessive speculation.
This document should be used by authors, reviewers, and area chairs in order to develop a common understanding of important ethical principles for CVPR. While other related conferences have spent significant effort, and dealt with associated controversies, this is the first time CVPR is attempting to address such questions formally, and this year the intent is to increase awareness in the community and acquire experience for authors, reviewers, and organisers. The primary goal is to encourage authors to incorporate discussion of potential harms (Section 2) and ethical considerations (Section 3) into the paper.
Reviewers will be asked to comment on whether negative impacts and other ethical issues have been appropriately reflected upon in the paper. Such discussion will contribute to a positive impression of the paper. Even though lack of discussion does not constitute grounds for rejection, reviewers can flag glaring violations of ethical principles (no matter whether such a discussion is present). These flags will be reviewed by an ombudsperson. In rare situations, CVPR program chairs, advised by the ombud, reserve the right to reject submissions that have violated key ethical principles, e.g., including clearly offensive content or working on human subject data without an appropriate IRB approval.
2. Potential Negative Societal Impacts
Submissions to CVPR are strongly encouraged to include a discussion about potential negative societal impacts of the proposed research artifact or application. (For CVPR 2022, this corresponds to Question 3 of the Submission Form). Whenever these are identified, submissions should also include a discussion about how these risks can be mitigated. Note that although there is no formal requirement to include such discussion for CVPR 2022, reviewers and area chairs are asked to positively weigh serious attempts to provide a discussion.
Grappling with ethics is a difficult problem for the field, and thinking about ethics is still relatively new to many authors. Given its controversial nature, we choose to place a strong emphasis on transparency. In certain cases, it will not be possible to draw a bright line between ethical and unethical. Therefore, a paper discussing any potential issues, should aspire to a broader discussion that engages the whole community.
A common difficulty with assessing ethical impact is its indirectness: most papers focus on general-purpose methodologies (e.g., object recognition algorithms), whereas ethical concerns are more apparent when considering deployed applications (e.g., surveillance systems). Also, real-world impact (both positive and negative) often emerges from the cumulative progress of many papers, so it is difficult to attribute the impact to an individual paper.
The ethics consequences of a paper can stem from either the methodology or the application. On the methodology side, for example, a new adversarial attack might give unbalanced power to malicious entities; in this case, defenses and other mitigation strategies would be expected, as is standard in computer security. On the application side, in some cases, the choice of application is incidental to the core contribution of the paper, and a potentially harmful application should be swapped out (as an extreme example, replacing ethnicity classification with bird classification), but the potential mis-uses should be still noted. In other cases, the core contribution might be inseparable from a questionable application (e.g., reconstructing a face given speech). In such cases, one should critically examine whether the scientific (and ethical) merits really outweigh the potential ethical harms.
A non-exhaustive list of potential negative societal impacts is included below. Consider whether the proposed methods and applications can:
- Directly facilitate injury to living beings. For example: could it be integrated into weapons or weapons systems?
- Raise safety, privacy, or security concerns. For example: is there a risk that applications could cause serious accidents or open security vulnerabilities when deployed in real-world environments? Would they make public people’s identity or other personal information without their consent?
- Raise human rights concerns. For example: could the technology be used to discriminate, exclude, or otherwise negatively impact people, including impacts on the provision of vital services, such as healthcare and education, or limit access to opportunities like employment? Please consult the Toronto Declaration for further details.
- Have a detrimental effect on people’s livelihood or economic security. For example: Have a detrimental effect on people’s autonomy, dignity, or privacy at work? Could it be used to increase worker surveillance, or impose conditions that present a risk to the health and safety of employees?
- Develop or extend harmful forms of surveillance. For example: could it be used to collect or analyze bulk surveillance data to predict immigration status or other protected categories, or be used in any kind of criminal profiling?
- Severely damage the environment. For example: would the application incentivize significant environmental harms such as deforestation, hunting of endangered species, or pollution?
- Deceive people in ways that cause harm. For example: could the approach be used to facilitate deceptive interactions that would cause harms such as theft, fraud, or harassment? Could it be used to impersonate public figures to influence political processes, or as a tool of hate speech or abuse?
3. General Ethical Conduct
We assume that all submissions adhere to ethical standards for responsible research practice and due diligence in the conduct.
If the research uses human-derived data, consider whether that data might:
- Contain any personally identifiable information or sensitive personally identifiable information. For instance, does the dataset use features or label information about individual names? Did people provide their consent on the collection of such data? Could the use of the data be degrading or embarrassing for some people?
- Contain information that could be deduced about individuals that they have not consented to share. For instance, a dataset with medical image annotations by experts could inadvertently disclose user information such as their name, depending on the features provided.
- Encode, contain, or potentially exacerbate bias against people of a certain gender, race, sexuality, or who have other protected characteristics. For instance, does the dataset represent the diversity of the community where the approach is intended to be deployed?
- Contain human subject experimentation and whether it has been reviewed and approved by a relevant oversight board. For instance, studies predicting characteristics (e.g., mental health status) from human data (e.g., performance of everyday activities) are expected to have their studies reviewed by an ethical board (IRB or equivalent).
- Have been discredited by the creators. For instance, the DukeMTMC-ReID dataset has been taken down and it should not be used in CVPR submissions.
In general, there are other issues related to data that are worthy of consideration and review. These include:
- Consent to use or share the data. Explain whether you have asked the data owner’s permission to use or share data and what the outcome was. Even if you did not receive consent, explain why this might be appropriate from an ethical standpoint. For instance, if the data was collected from a public forum, were its users asked consent to use the data they produced, and if not, why?
- Domain specific considerations when working with high-risk groups. For example, if the research involves work with minors or vulnerable adults, have the relevant safeguards been put in place?
- Filtering of offensive content. For instance, when collecting a dataset, how are the authors filtering offensive content such as pornographic or violent images?
- Compliance with GDPR and other data-related regulations. For instance, if the authors collect human-derived data, what is the mechanism to guarantee individuals’ right to be forgotten (removed from the dataset)?
This list is not intended to be exhaustive — it is included here as a prompt for author and reviewer reflection.
4. Final Remarks
In summary, we strongly encourage CVPR submissions to include discussion about potential harms, malicious use, and other potential ethical concerns arising from the use of the proposed approach or application. We similarly encourage authors to include a discussion about methods to mitigate such risks. Moreover, authors should adhere to best practices in their handling of data. Whenever there are risks associated with the proposed methods, methodology, application or data collection and data usage, authors are encouraged to elaborate on the rationale of their decision and potential mitigations. Reviewers and area chairs will be asked to positively weigh the depth of such ethical reflections