5 Key Ethical Challenges in AI Deployment

AI has become so much a part of our lives that it is becoming difficult to imagine our lives without it. Even search engines now have integrated AI within their interface to generate quicker results that can be grasped within seconds. The idea of collating data from hundreds of sources and cohesively presenting them is the genius of AI. If AI has this impact on non-professionals, it can well be imagined what importance it holds for enterprises and businesses that use AI to generate content.

So far, it seems like AI is the best thing to happen in the technological scene after quite some time. However, it is not that simple. Since AI can be ‘taught’ things by feeding it data, what happens if it chooses corrupted information over the objectively true one? It would be ethically mortifying because it would mean toying with the trust of people looking for neutral information. Let us look at the five most significant ethical challenges that must be borne in mind during AI development.

The Key Challenges

There are a lot of ethical concerns with AI, but these are the ones that are the most pressing:

1.   Probable promotion of Injustice

As learning systems, AI has to process chunks of historical data. However, can it be trusted to treat all data equitably, reach a just conclusion and present it to the viewer? Historical evidence on AI performance infers otherwise. It has been seen that the AI outcomes have had quite mixed effects on marginal groups who had been served up with some biased data. The best practice that can fight this challenge for now is to be sensitive enough to find anomalies, as did Amazon. The e-commerce giant found out that its AI system that shortlisted candidate CVs was skewed towards men. To deal with such discrimination against women, it discontinued the responsible AI model.

2.   Issues concerning human autonomy

It has been observed historically that certain algorithms have been used by social media platforms to collect data points on people, These data points capture snatches of information that can be used to map an individual’s political leanings, spending patterns, interests, etc. Therefore, by collating the data and mapping a particular population, a lot could be gauged about the population’s mindset. Now, if one reads the room and wants to manipulate it, one knows exactly what content to run so that public opinion can be manipulated. That is a dangerous privacy breach, and AI-platform owners must be held accountable for it.

3.   Allocation of work

Allocation of work is a serious concern raised by sci-fi writers – about human jobs being usurped by robots and AI. Although that has not been fleshed out exactly, the onus is now on company administrations on how to allocate work. The crisis arises because the entire work dynamics is put in disarray with AI being able to carry out complex jobs done by skilled employees. Ideally, this AI revolution should work in tandem with an advanced market that needs more sophisticated work than before. In that case, the erstwhile skilled work would become a basis for more advanced work, which is to be done with employees skilled and trained to that end. Otherwise, AI is bound to create mayhem in which individuals would struggle to maintain their relevance in the workplace, stressfully acquiring new skills to somehow avoid getting fired.

4.   Existential dread

AI has the effect of a spectacle in the eyes of common people, who find it a work of marvel that something non-human can intelligently talk to them. It has often been surmised if AI really is the future of the human race – whether a few hundred years from now human beings will become humanoids ruled over by superintelligent AI. This apparent loss of free will has become a dystopian nightmare for people who have already seen a tremendous advancement in technology.

5.   Avoiding blunt apprising

Often, care is not taken as to how AI should express the outcome of a process. However, any consumer has the right to explain to them the reason for an outcome. For instance, if a loan application with an NBFC is disapproved (utilising a system led by AI), the reason for such an outcome should be explained. AI should not come across as one working with a mystic logicality that is inaccessible to the common people. Explainability is a basic requirement that must be fulfilled, as mandated by the data protection regulatory agency in Europe, i.e. GDPR.

Conclusion

Thus, the AI used toward different ends in the online marketplace must follow a certain set standard so that it does not feel unintelligible to people. AI must be made familiar to people in general so that there is no undue fear about it. Also, AI must be used responsibly and towards the betterment of humankind, and not to fulfil an agenda that has no connection with scientific advancement.