Ethical frameworks for AI have been in talks for some time now. From Google and BMW to the government of Canada, everyone is framing guidelines for ethics in AI. And no matter how much time and money they invest in designing the ethics of Artificial intelligence, they wouldn’t be effective until they understand the crux of it. Researchers have proven that ethical frameworks for AI give an outward appearance that everything has been sorted. But if you look closely, you will find that AI poses serious threats. The ethics of artificial intelligence are a great marketing gimmick, and companies will have to think twice before implementing them.
What should companies do to prevent AI from causing harm?
Ethics in AI is a vast subject, and this article’s objective isn’t to deter you from researching better ethical frameworks for AI. Instead, you should not just stop at discussing the ethics of artificial intelligence. It would be best if you also observed the practical implementation of ethics in AI. Keeping detailed notes, maintaining journals of observable data, and periodic talks with data scientists and field experts are necessary. This strategy will help you to collaborate theoretical knowledge with what is happening on the ground.
Ethical frameworks for AI is a multidisciplinary issue. It would be best if you focused on various aspects — legal, scientific, technical, and risk.
The following points describe what you can do to check the ethics of artificial intelligence regularly.
- To check the fairness of ethical frameworks for AI
Fairness means measuring algorithmic disruption. After all, algorithms are written by humans, and it’s challenging for us to account for all the factors, which can cause AI to be biased. An excellent way to crosscheck is by studying the loopholes in housing, credit, and employment law. You can check for factors such as:
- Marginal effect
- Impact ratio
- Standardized mean difference
These ratios/terms will help data scientists learn at what points AI frameworks create bias and ensure no discrimination.
- To check the privacy of ethical frameworks for AI
Privacy has become an essential issue across all domains and more so for AI. Although ethics in AI ensure that privacy is maintained, it’s far from reality. An excellent way to measure the privacy of ethical frameworks of AI is “Differential Privacy.”
Differential privacy is a process in which random noise is entered into a system so that AI algorithms can derive essential details from a data set without further compromising the data. For example, a data set contains a list of alcoholics — name, age, workplace, and the trouble they caused. For hiding the person’s identity, random noise will be put into the system so that the AI algorithms don’t glean private data-name, ages, and workplaces. Instead, they work only on the trouble that the alcoholics caused to life and property.
3. Things to focus on to ensure ethics in AI
There is a need to study the ethics of artificial intelligence and to improve them. But one thing that all of us need to remember is that sometimes it becomes challenging to quantify ethical frameworks for AI. There is no denying that AI decision-making is a complicated process that requires a lot of research. And there is always a possibility that AI-controlled machines can adversely impact life and property. Companies need to focus on the following three things if they want to ensure ethics in AI:
- Proper documentation
- Mechanisms for accountability
- Model inventories
Data scientists and machine learning experts need to develop better models for ethical frameworks in AI because they can spot problems even before they originate. This process is necessary for deploying AI responsibly.
It’s time that companies start developing better ethical frameworks for AI. There are various reasons for it:
- Ethics in AI ensure fairness.
- Ethics in AI guarantee privacy.
- The ethics of artificial intelligence avoid distorted conclusions.
- The ethics of artificial intelligence warrant that users aren’t harmed due to improperly developed AI algorithms.
If companies don’t take the lead in developing ethical frameworks for AI, then courts or other legal entities might take the responsibility into their hands. Developing ethics of artificial intelligence requires investing time, resources, and brainpower. But it has several advantages. The future is all about AI, and if you want to emerge as a market leader, you must keep all the above points in mind while developing an ethical framework for AI.
Nishant likes to read and write on technologies that form the bedrock of modern-day and age like web apps, machine learning, data science, AI, and robotics. His expertise in content marketing has helped grow countless business opportunities. Nishant works for Sage Software Solutions Pvt. Ltd., a leading provider of CRM and ERP systems to small and mid-sized businesses in India.