How do you manage the social impact of machine learning?

1 Understand the context

Before you start building your ML model, you need to understand the context and the problem that you are trying to solve. Who are the stakeholders, the users, and the beneficiaries of your solution? What are their needs, preferences, and values? How will your solution affect their lives, rights, and opportunities? What are the potential risks and harms that your solution might cause or exacerbate? By answering these questions, you can define the scope, the goals, and the constraints of your ML project, and avoid unintended consequences or misuse.

2 Involve diverse perspectives

ML is not a neutral or objective technology. It reflects the biases, assumptions, and values of the people who create and use it. To ensure that your ML solution is inclusive, respectful, and relevant, you need to involve diverse perspectives in every stage of your ML project. This means consulting with experts, stakeholders, users, and affected communities, and incorporating their feedback and input into your design and evaluation. It also means ensuring that your team is diverse and representative of the people you are serving, and that you foster a culture of openness, collaboration, and accountability.

3 Evaluate the impact

Before you deploy your ML solution, you need to evaluate its impact on the people and the society that it affects. This means testing your solution for accuracy, reliability, and robustness, and identifying and mitigating any sources of error, bias, or discrimination. It also means measuring and monitoring the outcomes, benefits, and costs of your solution, and assessing its alignment with ethical principles and social norms. You should also plan for contingencies, such as how to handle errors, complaints, or feedback, and how to update or withdraw your solution if needed.

4 Communicate transparently

One of the key aspects of managing the social impact of ML is to communicate transparently with your stakeholders, users, and affected communities. This means explaining the purpose, the functionality, and the limitations of your ML solution, and disclosing any relevant information, such as the data sources, the algorithms, the assumptions, and the risks. It also means providing clear and accessible ways for people to interact with your solution, such as how to use it, how to control it, how to opt out of it, or how to challenge it. By communicating transparently, you can build trust, confidence, and accountability for your ML solution.

5 Learn and improve

Managing the social impact of ML is not a one-time task. It is an ongoing process that requires continuous learning and improvement. You should always be open to new information, feedback, and insights that can help you improve your ML solution and its impact. You should also keep up with the latest developments, trends, and best practices in ML ethics and social impact, and learn from the experiences and challenges of other ML practitioners and researchers. By learning and improving, you can ensure that your ML solution is relevant, responsive, and responsible.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?