Can you answer these AI ethics questions?
Tackling difficult AI ethics questions with simple thinking frameworks
Let’s play a game.
I’m going to ask you ten questions about AI. For each question that you feel you have a solid answer to, give yourself 2 points; for each question that you don’t have an answer to but have given some thought to, you get 1 point; and if you have no clue and have never thought about it, then you get no points.
Count your total score, and then I’ll tell you mine. Let’s see who wins.
Ready?
Here are the 10 questions:
How can we ensure AI systems are free from biases, and what mechanisms can be put in place to detect and mitigate biases that arise?
How can AI systems be made transparent so that users can understand and trust their decisions?
How do we balance the benefits of AI with the need to protect individuals' data (privacy)?
Who should be held responsible when an AI system makes a mistake or causes harm? Is it the developers, the company that deployed the system, the end-users, or a combination of stakeholders?
How do we prevent AI from becoming a tool for excessive surveillance and control, potentially encroaching on individual freedoms and rights?
What policies should be implemented to manage the economic impact of AI, particularly concerning job displacement?
AI autonomy vs. human oversight: To what extent should AI systems operate independently? Where do we draw the line for human oversight?
Ethical data usage: How can we ensure that the data used in training AI systems is ethically sourced and used?
How can AI be developed and deployed in ways that respect cultural and ethical differences across and within societies?
What frameworks and institutions are necessary to govern the rapidly advancing capabilities of AI at both national and international levels?
What was your score? Mine was nine. And I didn’t get two points on a single one. Actually, very few people would. These are hard questions with many points of view, depending on who you ask.
This is so much so that according to a survey of 100 Fortune 1,000 IT leaders, 100% are concerned about the security risks of AI, and 98% have paused generative AI (GenAI) projects as a result. About 42% said they are worried about the ethics of the GenAI, particularly the inherent societal biases of training data (26%) and lack of regulation (26%).
Unless you’re an AI ethicist, this is new territory for you—as it is for most company executives now trying to make decisions about implementing AI tools.
So, how should we approach these complex questions to make ethical decisions?
Let’s start by looking at some common approaches and two frameworks for considering tough ethical questions about AI. In the end, I provide two useful checklists for applying these frameworks.
Three Approaches to AI Ethics
I see three common ways AI ethics is approached in the world today: as a technologist, an industrialist, or an ethicist.
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