6 Ethical Considerations of Artificial Intelligence

6 Ethical Considerations of Artificial Intelligence

Artificial intelligence (AI) is taking the world by storm. AI uses technologies such as machine learning (ML) and natural language processing (NLP) to help people make more informed decisions and streamline repetitive tasks and processes.

The incorporation of AI into organizational processes can enhance productivity and foster creativity. However, as AI becomes a bigger part of the way we work, ethical considerations become even more important to consider.

By learning some basic ethical standards of AI, you’ll be better able to avoid potential biases, uphold standards for data privacy, and properly regulate the deployment of AI technology. You’ll also be better able to recognize the importance of training employees on what AI is and what it isn’t, and know how to create effective policies regarding the use of AI within your organization.

This article will cover ethical issues in AI in detail, equipping you with important information to properly approach and implement AI in your organization.

1. Ethical issues in AI decision-making

AI systems and algorithms can influence human decision-making processes, and there are ethical challenges related to automation, decision-making, and transparency. Without proper standards in place, AI systems can make unfair or biased decisions that may reinforce stereotypes, violate user privacy, or even create human rights concerns.

This happens, in part, because of the way AI develops through machine learning. Machine learning algorithms are trained to make predictions or classifications based on the data the user inputs. Over time, the system can train and improve the way it functions based on the data it’s already processed.

While machine learning is an incredible form of technology, it can present an ethical dilemma when the system is trained on a pool of data that isn’t large or diverse enough to meet the system’s needs.

Example

If an AI algorithm has been trained on how to recognize faces using primarily pictures of white people, it may be more likely to recognize a white person than a person of a different race. This can create discrimination and racial bias between different groups of people. For example, a black reporter in New York City recently discovered that an AI-powered facial recognition system couldn’t detect her face.  

Solution

For the situation above, the best solution is to include training and testing data that is broad and diverse enough to cover all use cases. More specifically to our linked example above, images of black women (as well as other ethnicities) should have been incorporated into the AI’s training and testing data. This way, the system could better recognize people of various colors, races, and ethnicities.

Another possible solution is implementing greater transparency and explainability mechanisms into the AI algorithms. Explainable artificial intelligence (XAI) encompasses a set of methods that provide further insight into the data produced by the algorithm, leading to greater trust and comprehension of the results.

You can also make sure the decision-making process using AI systems is understandable and traceable, allowing for easier identification and rectification of biases. It’s also wise to include some level of human involvement or control in decision-making to limit the possibility of AI-related errors.

2. Data privacy and protection in AI

There are ethical concerns surrounding the use of personal data and data sets in AI models. In addition to its testing and training data, AI has access to any data you share. If this data isn’t properly treated or protected, it could cause a major breach of privacy.

You must include data privacy regulations and safeguards to ensure ethical AI development, especially if the data involved is sensitive personal information such as biometrics, individual financial history, or data with potential legal effects.

There’s also a significant need for auditing and accountability in AI algorithms, especially considering the experiences of tech giants like Amazon and Microsoft. These systems can allow continued innovation in AI while exposing any potential concerns and mitigating their impact.

Finally, keep in mind that ChatGPT and similar AI models can use all the information you share when training their next iterations. This includes any proprietary or personally identifying information you include in your prompts. If users of a future model ask the right questions, they may gain access to the data you shared with an earlier model.

With this in mind, robust policies regarding AI use are necessary. These rules and regulations should cover what AI can and cannot be used for. They should also cover what information can and cannot be shared with AI. In situations when companies own proprietary AI models that might require access to sensitive data, there should be security policies in place that cover how this data will be handled.

Example

In April 2023, a group of engineers at Samsung accidentally leaked sensitive company information to ChatGPT. Their goal was to improve their own source code, but now OpenAI has access to all of the data they shared. As a result, Samsung has put stricter policies in place about the use of AI and is working on its own AI systems for future employee use.

While some factors may always be outside of your sphere of influence, you will always have control over your own behavior. You can control what data you give AI while also implementing safeguards such as training across your company to support the best possible data privacy and protection. Companies should also make sure that any third-party providers they work with also take these protective measures seriously.

Solution

To ensure data protection and user security, you must develop robust data protection protocols. This could include the appointment of privacy officers, ongoing privacy impact assessments, and more thorough product planning during initial development. In addition, employees should have training about how to effectively protect data within systems while adhering to the strictest data privacy regulations.

Finally, you can implement anonymization techniques and data encryption to ensure that personal data used in AI models is always kept confidential and secure. For example, you can create modification techniques such as encryption and word or character substitution to guard data. This may sound like a small shift, but it can have a tremendous impact.

3. AI in health care: Balancing benefits and risks

AI can potentially help with diagnosing and treating illnesses. At the same time, great care is necessary to ensure that patient well-being and privacy aren’t at risk. Without high regard for ethical standards, doctors may provide inaccurate diagnoses or treatment plans while also facing some of the same issues that everybody faces, such as privacy and security concerns.

Health care professionals must also learn how to educate their patients about the use of AI in developing treatment plans so they can provide informed consent when necessary.

Example

Imagine that an oncologist is using an AI algorithm to process a patient’s past medical records and explore different cancer treatment options. If the system was only trained with a few hypothetical cases, the system can easily produce inaccurate information, especially since it only has context from examples. When the stakes are so high, ensuring the reliability and validity of the results produced is critical.

Patient information might also be leaked if proper precautions aren’t put in place. For example, Atrium Health experienced a data breach in 2018 that released the names, addresses, Social Security numbers, and important clinical information of nearly three million people. The problem was eventually traced back to third-party vendors in addition to the AI technology and machine learning put in use.

Solution

Create a framework that balances the benefits of AI in health care with patient privacy and well-being. Strive for clear consent procedures, robust data anonymization, and compliance with medical ethics to ensure that AI technologies enhance patient care without compromising personal health information or outcomes.

Finally, make sure that any systems in use are properly trained and developed to produce high-quality and accurate information that doesn’t put patient well-being at risk.

4. Social and cultural implications of AI

Generative AI and chatbots have the potential to affect content creation for social media and other media outlets. It also poses ethical challenges with the presence of content such as “deep fakes,” artificially created photos and videos that make fake events appear to have really happened. Ethical concerns are also important when it comes to facial recognition to ensure proper levels of privacy, safety, and diversity are taken into account.

Example

In 2019, a deepfake video was made of Facebook founder Mark Zuckerberg. The video used past footage of Zuckerberg and replaced his voice with an actor’s. Zuckerberg seemingly claims in the video that he has sole access to stolen data from billions of people. Since Facebook has nearly three billion active users each month, it’s easy to see why a video like this could cause great concern.

Solution

You can start by promoting responsible AI content generation by clearly labeling AI-generated content and ensuring its distinction from human-generated content. This will help you avoid any false perceptions that can be potentially damaging.

It may also help to develop advanced tools for detecting deep fakes and misleading information, empowering users to identify potential manipulations. Some easy signs to look for include disproportions between a person’s body and face as well as awkward body posture or movements.

5. Legal and policy frameworks for AI ethics

Policymakers such as organizations like the European Commission, the European Union, and the US National AI Initiative play an important role in shaping ethical AI regulations. They work to develop transparent and explainable AI models that ensure alignment with ethical principles. As AI continues to develop, we’re likely to see ever more strict and comprehensive laws put into place to govern the use of AI.

Example

In late 2022, Kristina Kashtanova produced a graphic novel called “Zarya of the Dawn” and registered a copyright for their work. The US Copyright Office canceled the request after learning that Kashtanova had created the book using Midjourney, an AI-generative tool. Later in 2023, the copyright office granted revised copyright protection that extended only to the human-generated components of Kashtanova’s work.

Solution

Collaborate with policymakers and international organizations to establish comprehensive legal frameworks for AI ethics. This includes guidelines for data usage, algorithm transparency, accountability, and public oversight of AI systems. Ideally, you’ll have a team working together that can spot potential issues, plan how to address conflicts, and ensure that you’re always fully compliant and up-to-date on new and emerging laws.

6. AI in criminal justice

When AI is used in criminal justice, a concern about fairness and bias can arise. For starters, facial recognition technology is much more likely to produce incorrect conclusions when trying to identify non-Caucasian men and women. Since flawed technology can encourage injustice, this is an essential ethical concern to address.

Example

In 2020, Robert Williams was falsely arrested because of an inaccurate facial recognition match. Although he denied any wrongdoing, the officer told him the computer said it was him. Facial recognition software used by the Detroit Police Department had matched William’s old driver’s license picture to a photo of a man accused of stealing watches from a luxury store. Williams was held overnight in jail, and filed a lawsuit after his release in hopes of banning the technology.

Solution

Williams’ story serves as a striking example of the negative consequences of unregulated AI in criminal justice. To prevent such issues, municipalities and law enforcement should ensure fairness and transparency in AI applications in all criminal justice systems. Legislators and other decision-makers should implement continuous monitoring and auditing to identify and rectify any biases that might emerge.

This could mean developing systems that are less reliant on historical data, more upfront about the processes they are using, and trained on large and diverse data sets.

Navigating ethical AI

There are many ethical concerns to keep in mind as you think about your use of AI and its impact on society, decision-making, and privacy. These concerns don’t diminish the potential value or impact of AI, but they also cannot be ignored as you look to establish procedures that will help ensure responsible use of the technology.

To address these ethical challenges, ongoing interdisciplinary collaboration and continued dialogue among stakeholders are paramount. Without this level of cohesion, it will be even more challenging to face the ethical challenges posed by new AI technologies.

If you’re interested in finding a job in AI, check out the open positions online at Upwork. You can also search through a database of qualified AI engineers to find a professional who can help you on your next project.

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6 Ethical Considerations of Artificial Intelligence
The Upwork Team

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