Artificial intelligence is transforming the way we work, live, and play. But as these intelligent machines increasingly penetrate our existence, we must ask some tough questions regarding the future. Let us talk about the true ethics challenges in artificial intelligence and how they impact us all – business executives to ordinary technology users.
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The AI Ethics Challenge
AI systems are increasingly making decisions with direct impact on people’s lives – such as granting loans and diagnosing disease. The difficulty is to have those systems be transparent, fair, and accountable.
Firms such as Boston Consulting Group describe ethical AI as not so much about the algorithms. It is a three-way challenge involving technical controls, corporate culture, and robust governance frameworks. A medical AI could be great on paper, yet if physicians are not going to trust its recommendations because they are unable to understand how it comes up with the recommendations, then that is a cultural issue that governance needs to fix.
Ethics Frameworks: A Guide Beyond Guidelines
With more than 200 AI ethics models globally, ranging from UNESCO’s human rights model to Singapore’s economic emphasis, it’s obvious there’s no single model. But themes are common: transparency, accountability, and fairness.
These are not high-sounding terms. The NIST AI Risk Management Framework is helpful to discover and remediate risks throughout an AI system’s lifetime. Such frameworks become increasingly vital as AI becomes more potent.
The Regulation Environment: Safeguarding Without Suppressing Innovation
Should regulations for AI be specific to industries or the same for all? The EU AI Act follows a risk-based approach, categorizing AI applications into four tiers and imposing stringent controls on high-risk applications such as social scoring systems.
Conversely, the US has a divergent approach, with different agencies handling AI in their own domains – the FDA for healthcare devices and the FTC for consumer use.
Others have contended that we do not require new laws to tackle all AI issues. The Royal Society points out that current law of liability would generally work well when AI causes harm, as long as developers can explain why their systems made particular decisions along the way.
The Black Box Problem
One of the biggest ethical problems with AI is the “black box” problem. This is when an AI makes decisions we can’t account for. This lack of transparency is a massive trust problem.
This is being tackled by recent developments in explainable AI (XAI). Methods like LIME produce explanations for specific AI choices and allow businesses to meet the requirements of regulations like the GDPR’s Article 22, giving people the right to appeal against decisions that have been automatized.
IBM’s AI Fairness 360 toolkit is one example of a free tool that looks for hidden biases in AI models related to gender, race, or money issues.
The Environmental Price of AI
The ethics argument isn’t simply an argument about being equitable – but also about sustainability. Training one big language model such as GPT-3 produces about 552 metric tons of CO₂ – or the same as 120 cars driving for a year.
This is where green technology intersects with AI ethics. Google and other such companies are developing energy-efficient hardware called Tensor Processing Units (TPUs) to lower the carbon footprint of AI. Even better, AI can be used to optimize renewable energy systems to be more efficient. DeepMind used reinforcement learning to reduce the consumption of cooling energy in Google’s data centers by 40%.
The Future of Ethical AI.
In the future, ethics issues in artificial intelligence will only increase as these computers get stronger and more widespread. The test for everyone – from technology creators to business users to consumers – is to weigh new ideas against responsibility.
The World Economic Forum further states that investment in green technology can also yield additional benefits. For every ton of CO₂ reduced locally by clean energy measures, we can avoid an additional 2.4-2.9 tons worldwide by technology transfer. Smart grids powered by AI and precision agriculture lead the way towards increased efficiency with utmost regard for equitable access to all.
Starting Off with Ethical AI
If you are either an AI consumer or a business leader and are an AI user, there are free resources that can help you better grasp these ethical issues.
- Online courses like AI Ethics in Action from the University of Helsinki (on Coursera)
- Utilize resources such as Microsoft’s Responsible AI Toolbox to learn through doing.
- Government policies like the NIST AI Risk Management Framework
Bottom Line
The ethics questions in artificial intelligence aren’t big questions to debate – they affect actual companies, customers, and society. By exploring these questions now, we can create an AI future that benefits everyone. As AI becomes a part of our everyday lives, the decisions we make today will determine whether these powerful technologies will benefit us or hurt us in the years to come. We don’t need to fear AI but instead use it as an extension of our best selves, not our worst biases.