What’s Wrong with AI: Ethical Considerations in AI Development

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Introduction
Bias and Fairness
Transparency and Explainability
Privacy and Data Protection
Accountability and Responsibility
Safety and Reliability
Closing Thoughts on Ethical Considerations in AI Development

What’s Wrong with AI: Ethical Considerations in AI Development

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cdteliot

5 min read

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As artificial intelligence continues to advance rapidly, ethical concerns surrounding its development and deployment have come to the forefront. From bias and discrimination to privacy and accountability, AI systems raise complex moral questions that demand careful consideration by developers, policymakers, and society at large.

Bias and Fairness

If you think only humans have biases, think again. Even artificial intelligence, with all its logical glory, can have questionable views. Of course, it would be prejudiced since it's trained on material made by humans who have their own inclinations

. AI has read and processed generations of data riddled with past inequalities in society, so without any better judgment, it may copy what has been wrongly practiced in its tasks

. It can perpetuate negative partiality because it can generate adverse outcomes in significant areas like hiring, lending, and criminal justice

. An example is when an AI hiring tool goes through your company records and only shortlists the applicants whose profiles are similar to the majority of current employees. It will unconsciously make decisions that discriminate against gender or race

. To counter such biases, AI algorithms should undergo rigorous testing and validation. AI must be trained to encourage diversity and shut down misogyny. Once the right programming and perimeters are established, its tendency to succumb to prejudice will be reduced

4 sources

Transparency and Explainability

eastgate-software.co...

A lot of AI tools and software are like "black boxes"— solid, opaque fortresses where everything is closed off and you never know what's going on inside

. It's not really clear how a lot of AI systems scrape data, analyze information, and come up with their outputs. The public is kept in the dark about what's happening behind the scenes

. This lack of transparency makes it difficult for people to determine the methods AI uses to train itself and make decisions. Since the internal workings and behavior of AI models are mostly concealed, you wouldn't be able to ask the companies behind them for accountability if they do trespass into intellectual property and privacy

. You can't question them on the logic and data sources they use. Thankfully, some organizations and groups have stepped up to monitor AI and give the masses more information on the mechanisms it runs on

. Researchers are developing techniques like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) to explain model predictions

. You can also track data origins and transformations using some data provenance tools

7 sources

Privacy and Data Protection

bairesdev.com

How sure are you that your data is safe from AI? With social media's popularity, you must have published one or too many posts on the Internet. You also give away your information to create online profiles and accounts. AI considers the content and information you put out publicly (and even privately for shady AI companies) to be up for grabs

. The worst thing is it won't ask for your permission every time it snatches data from you. Without your knowledge, you're always at risk of potential data breaches, unauthorized access to sensitive information, and invasive surveillance

. You must protect yourself from these unfavorable possibilities by reinforcing your accounts with as many security measures as you can and by being vigilant and selective with what you publish and share on social media platforms

. You must also read the privacy policies of websites you're signing up for, so you're aware if they use AI and if you're basically giving them access to your data

. For organizations, it's best to invest in privacy-fortifying technologies such as federated learning and differential privacy to still enable AI while protecting users' information

6 sources

Accountability and Responsibility

extremetech.com

Artificial intelligence is a very secretive form of technology. Sure, we know the basics about its algorithms and functions, but we don't have the full picture. That's why when AI compromises your data, you'll find it hard to demand accountability from AI developers and vendors. Someone should be held liable when AI commits errors or causes harm, but there are not enough laws and regulations out there that protect people from such violations

. Without anything to stop AI from getting out of hand, it will continue to trample on people's privacy and property. It should be up to AI companies to regulate their own systems, establishing boundaries and assuring users of their safety. They should also be open about how they train their AI models and where they get their data from

. More importantly, governments and relevant authorities must catch up and secure their citizens from AI's exploitation by implementing laws and sanctions that will keep developers in check

. As a regular user, you should also do your part and read any fine print so you won't unwittingly allow AI to seize your data. Once you've given your consent, even if there are regulations in place, you won't be able to object if your content and information get nabbed. Always be alert and defend yourself from threats that come with AI use

5 sources

Safety and Reliability

AI has been integrated into a lot of industries and it's been given more and more responsibilities as it improves. This is a positive thing because AI can help in a lot of processes, lower costs, and allow human professionals to focus their attention on more crucial obligations

. However, this means that if AI messes up, jobs and lives are on the line. High-stakes applications of AI in areas like healthcare, autonomous vehicles, and critical infrastructure mean that the slightest error can be dangerous and potentially catastrophic

. A technical glitch or a sudden system hacking may cause efficient AI-powered tools and equipment to malfunction and crash. The best solution to such risks is to incorporate fail-safe mechanisms as a safety net for accidents and unforeseeable circumstances

. This also proves that human oversight is still essential to make sure AI is doing its job well, monitoring if it's in good condition before and during operation. Moreover, standards and best practices for AI safety must be established since these inform people about what they should do in case a system failure occurs

5 sources

Closing Thoughts on Ethical Considerations in AI Development

Ethical concerns regarding the development of AI must be taken seriously. This is a matter of great importance and not just a fleeting afterthought or a trivial consideration that can be continuously ignored. It's a fact that AI continues to take up a bigger role in society, so the people and companies backing it should do more than just bat an eye whenever it does something wrong.

Issues like bias, transparency, privacy, accountability, and safety require ongoing attention and proactive solutions. Developers and policymakers alike must regulate AI so it doesn't become more of a problem than it already is.

Everyone must be educated on this topic since public awareness is what pushes authorities to put their foot down and say enough is enough. We hope, though, that it won't have to come to that and those responsible will act before a mass outcry against AI shoves them into action.