Ethical considerations for research software

Last updated on 2024-06-30 | Edit this page

Estimated time: 60 minutes

Overview

Questions

  • What ethical considerations are there when using and writing research software?
  • How can our research software impact the rights and well-being of others?

Objectives

After completing this episode, participants should be able to:

  • Understand some ethical issues around research software development and usage and how they impact others.

Research software development and its use (along with data it handles) does not happen in a vacuum - it can pose a number of ethical concerns and challenges for society and the environment, such as contributing to social inequality, compromising privacy and security, increasing environmental impact, and creating social and psychological issues. While these concerns do not strictly fall under the FAIR principles, they all contribute to research software quality, reliability and responsibility.

Some of the ethical considerations around software are listed below.

Software development practices

  • Transparency: openly sharing source code and methodologies to allow for scrutiny and replication of research.
  • Software quality and reliability: rigorous testing to ensure that software performs as documented and produces reliable results.
  • Accountability: ensuring that developers are accountable for the software they create, particularly in terms of accuracy and reliability.

Ethical software reuse

Good research software practices apply not only when we develop software ourselves, but also when we reuse other people’s software. Discuss as group and write down in the shared document your thoughts about what ethical considerations should we have in mind and pros and cons we should weigh in when choosing other people’s software for reuse.

Here are some considerations:

  • How is software shared? Is it openly available on some code sharing platform or given to us on a memory stick?
  • Are developers of the software credited for their work?
  • Are software developers respectful to each other and other external contributors to or users of the codebase in their communication? Does the project have a code of conduct and contribution guidelines?
  • How is the project funded? Are there any concerns about who is financing the work?
  • Is the project active? How many open issues are there and how long have they been opened for?
  • When we are selecting tools and libraries to include in our research pipelines or software, we do have a duty to check the quality and reliability of those components. Are they well tested? Is the project using outdated methods? We can also look to other indicators of reliability - e.g. does the project have many open unresolved issues for bugs that might signal unreliability?
  • Do they support they users of their code and how is that support handled?
  • Are we respecting licenses and giving proper attribution to the developers of the code we are reusing?
  • When incorporating other researchers’ software into our research workflows we should not do it blindly but should inspect it critically and look closely at how the results of that component are generated.

Note: tools such as Snyk are available to help you check software projects that you intend to reuse in your work - they are designed to help you identify find weaknesses, violations, vulnerabilities and security issues in open-source software libraries and can help you make a decision whether or not you should use some software.

Intellectual property and licensing

  • Open vs closed source: balancing the benefits of open-source software and data with the rights of creators to protect their intellectual property and data privacy.
  • License compliance: ensuring that software use complies with licensing agreements and avoiding unlicensed (and illegal) use of software and data.

Intellectual property and licensing of AI-generated code

The licensing and intellectual property (IP) of code generated by large language models (LLMs) and AI pair programmers (such as CodeGen or Google Copilot which operate with an AI system as one of the code developers) raise complex legal and ethical questions that are not clear cut. Discuss as group and write down in the shared document some considerations and pros and cons of using AI-generated code.

Here are some considerations:

  • Intellectual Property (IP) ownership of the AI-generated code - typically, IP laws assign authorship to humans, not machines. However, the authorship of AI-generated code is ambiguous - it could belong to the developer who directs the AI, the developer’s employer or the entity owning the AI. Furthermore, AI pair programmers can generate results that match or closely match code that is publicly available and potentially already licensed. As a result, you might end up incorporating someone else’s (licensed) code into your code base without attribution or considering license implications. Some tools like GitHub Copilot have taken steps to reduce the risk of this by filtering out code completions that match publicly available code.
  • Intellectual Property (IP) ownership of the training data: if the AI model was trained on copyrighted code, the output could potentially be seen as a derivative work, raising IP issues regarding the training data’s licenses.
  • Licensing terms of the AI-generated code - under what terms is the generated code to be used, for example, if the AI model is trained on open-source code, the generated code might need to comply with the open-source licenses (e.g., GPL, MIT). How can we attribute the original authors of the code used to train the AI model on? In addition, ethically, it is good practice to acknowledge the use of AI tools in generating code, even if not legally required.
  • Security and bias concerns of AI-generated code - AI tools can generate code that is insecure, potentially destructive (e.g. deleting files) or use biased algorithms and data (see the section below) - so we should always try to understand and check any generated code before running it.

Responsible use of software & data

  • Dual use: recognizing and mitigating the potential for software to be used for harmful purposes (e.g. cybersecurity tools being used for hacking).
  • Social impact: considering the broader social implications of software applications and data used, e.g. the use of data that has been collected without permission from the groups or individuals (e.g. historically excluded and exploited groups), or data that contains private or upsetting information.
  • Bias mitigation: implement measures to detect and mitigate biases in algorithms and data, ensuring that the software treats all users fairly.

Scraping publicly available data responsibly

You have been tasked with writing software to scrape a public forum to collect personal data for your study - discuss as a group and write down what ethical issues should you consider and how should you act in this case?

  1. Compliance with Terms of Service (ToS) - most websites, including forums, have ToS that explicitly prohibit or restrict web scraping. Violating these terms can lead to legal action and is considered unethical.
  2. User privacy concerns - scraping a forum can collect a vast amount of user data, including personal information, opinions, and conversations. Avoid collecting personally identifiable information (PII) unless it is absolutely necessary and permissible and protecting user privacy (e.g. do not de-anonymize users).
  3. Lack of consent: users of the forum likely have not given explicit consent for their posts and data to be scraped and used by third parties. It’s ethical to inform users and seek consent where possible, or at least be transparent about data usage.
  4. Impact on website performance - scraping can put significant strain on the forum’s servers, cause Denial of Service attacks amd potentially disrupting service for regular users. Ensure your software is designed to be non-disruptive.
  5. Data use and sharing - clearly define and limit the purpose of data collection, collect only the data necessary for the intended purpose, and be cautious about how the scraped data is shared or sold. Ensure that it is used responsibly and does not harm the users from whom the data was collected.
  6. Data accuracy and integrity - ensure that the scraped data is used accurately and not misrepresented, avoid taking quotes out of context or using data in misleading ways, avoid modifying or altering the data in ways that could be deceptive.
  7. Legal considerations - forum content is often copyrighted so scraping content without permission can lead to copyright infringement so seek permission where necessary.
  8. Ethical purpose - consider the intent behind scraping the forum and ensure that the purpose is constructive and beneficial (and not harmful and unethical - e.g., for creating, fake profiles, spam, or harassment).
  9. Transparency and accountability - where possible, be transparent about your research methodology, scraping activities and their purpose, take responsibility for any negative consequences that may arise from scraping and be prepared to address and mitigate any issues.

Best Practices for ethical scraping:

  • Respect robots.txt: Always check and respect the robots.txt file of the forum, which indicates the site’s rules regarding web crawling.
  • Rate limiting: implement rate limiting to avoid overwhelming the server.
  • Data minimization: collect only the data that is necessary for your purpose.
  • User-Agent identification: use a User-Agent string that identifies your scraper and include contact information.
  • Ethical review: if you are scraping for research, consider submitting your methodology for ethical review.

Environmental impact of research software

  • Energy consumption: research software often relies on large-scale data centers for storage and computation, or the use of HPC systems and supercomputers for complex simulations and data analysis, which can significantly increase energy usage (much of which may come from non-renewable sources).
  • Carbon footprint: the electricity used to power data centers and HPC systems contributes to CO2 emissions, particularly if the energy comes from fossil fuels.

Using HPC systems in a more environmentally responsible manner

Often our research software is written for use on HPC systems. Discuss as a group and write down in which way can we improve our practices when writing, testing and running code on HPC system to support more responsible and less wasteful use of resources.

Here are some thing to consider:

  • To make best use of resources, part of our workflow should include identifying the most appropriate job configurations, e.g. job scaling or using workflow management tools like Snakemake to ensure that we do not repeat expensive steps unless some upstream step in our workflow has changed.
  • Avoid running untested code on a HPC system - test and debug locally first
  • Run a test job on one case study or a small data set before running on full data (consider setting up many jobs and finding 100s of jobs crashed after a few hours)
  • Avoid including an expensive data processing step in every job when it could be run once and set as a dependency for other jobs
  • Using many nodes for a job that is not designed to run in parallel - consider refactoring your code
  • Optimise your code and use more efficient algorithms

Technical topics:

  • Work-flow management
  • Profiling jobs on HPC
  • Profiling Python code

Further reading

We recommend the following resources for some additional reading on the topic of this episode:

Also check the full reference set for the course.

Key Points

  • To act ethically, we have to be both responsible producers and consumers of research software.
  • Free and open source research software and data, along with FAIR, ethical and environmental considerations, offer viable ways to make more transparent, reliable and accountable science that not only advances scientific knowledge but also respects and protects the rights and well-being of all users.