As the world of artificial intelligence (AI) expands, so do the ethical questions surrounding its use. In 2023, AI has the potential to transform businesses in numerous ways, enabling them to operate more efficiently, make better decisions, and provide better experiences to their customers.
However, with such expansive technological capabilities comes an increased responsibility to uphold ethical standards. Businesses should consider AI ethics throughout the acquisition, and implementation of AI solutions. Organizations must have a clear vision for how they plan to use their AI systems and exercise diligence in ensuring that their operations comply with the established ethical considerations.
This article will explore some of the main topics involved in AI ethics, the current initiatives and legal frameworks in place to manage AI usage and potential solutions to ensure the responsible use of this technology. Keep reading to learn how to produce and use AI while adhering to ethical standards.
Leading issues in AI ethics
Each of these elements is important to consider in order to ensure the safe, effective, and responsible use of AI.
Bias and discrimination in AI algorithms
AI algorithms are computer programs that use data to learn and make decisions. This data can come from a variety of sources, such as photos, text, or numbers. The organization and storage of this data is known as a dataset.
In machine learning and artificial intelligence, datasets are common tools for training algorithms to recognize patterns and make predictions. However, the data itself can sometimes be biased towards certain groups or characteristics.
For instance, if a dataset mainly consists of photos of men, the AI algorithm may not be able to recognize female faces as easily. This can lead to biased decisions that reinforce discriminatory attitudes. Bias can also be introduced by human programmers who have their own unconscious biases. Therefore, it is crucial that AI algorithms are designed to be fair and unbiased and that they are audited and monitored regularly to ensure this is the case.1
Privacy concerns and data protection
AI algorithms require substantial amounts of data to function effectively. This data can include personal information, and users may not be aware of what data is collected or how it is used. Privacy advocates and lawmakers have raised concerns about the potential misuse of this data, particularly by tech companies with histories of mishandling user data.1
In 2020, the Office of the Australian Information Commissioner (OAIC) issued a fine of $1.5 million to Facebook for mishandling user data. The OAIC found that Facebook had failed to take reasonable steps to protect its users' personal information from unauthorized access or disclosure. This resulted in the personal information of more than 300,000 Australians being exposed online. Organizations must design AI algorithms with privacy in mind, and users need control over their data to prevent future breaches.2
Autonomous decision-making and accountability
As AI algorithms become more advanced, a variety of industries and applications use this technology to automate processes and make decisions without human involvement. Banks and financial services use AI to detect fraud, medical organizations use AI for diagnosis and treatment recommendations and the retail industry uses AI to manage inventory and customer service. This raises questions about who is accountable when things go wrong.3
For example, who is responsible if an autonomous vehicle causes an accident? Or if an AI-based system makes a wrong decision in healthcare? And who is accountable when a customer faces legal trouble due to a decision made by an AI algorithm? It is important that responsibility is clearly defined and that AI algorithms are designed to make decisions that fall within ethical boundaries.
Current initiatives and legal frameworks
Several initiatives and legal frameworks seek to ensure the responsible use of AI. These regulations are in place to protect users and ensure that AI algorithms are fair, ethical, and accountable.
The European Union's General Data Protection Regulation (GDPR) is an example of a legal framework designed to protect data privacy.4 In relation to AI, the GDPR sets out requirements for transparency, data minimization and security.
The Institute of Electrical and Electronics Engineers (IEEE) Global Initiative on Ethics of Autonomous and Intelligent Systems is another important initiative that seeks to ensure the ethical use of AI.5 This initiative guides the responsible development and deployment of AI systems.
The AI Now Institute at New York University is an organization dedicated to researching the effects of AI on society and developing policy solutions. They produce reports and recommendations that address the concentration of power in tech companies and advocate for ethical standards to ensure the responsible use of AI.6
Potential solutions for the ethical challenges in AI
Ethical principles for business executives are now a standard in the tech industry and public policy circles. There are several steps organizations can take to ensure that they use AI responsibly, including the following:
Establish an ethical framework
Organizations should have clear policies on responsible AI use, including guidelines on privacy, data protection, bias prevention, and accountability. By prioritizing ethics at the start of a project, organizations can minimize risks and create transparent systems. For example, an AI-driven decision-making system should have a clearly stated ethical framework to ensure fair and just decisions.
Promote transparency and explainability
Experts advise developers to create AI systems that are transparent and accountable.7 This means designing algorithms with auditable data trails so that users can understand how decisions were made by the AI system. To achieve this, organizations can use open-source code and document their data pipelines.
Open-source code refers to a type of software whose original source code is freely available and can be modified and redistributed by anyone. Data pipelines are sequences of data processing activities that move data from one system to another. Implementing these changes can help organizations give users more autonomy and control over their data.
Prioritize user privacy and data protection
Companies should design AI systems with privacy in mind and take steps to ensure that user data is secure. This includes monitoring for potential data misuse and ensuring that users have control over who can access their data. Some best practices may include encryption and pseudonymization of data.
Encryption is a method used to convert plain text into a coded message that only authorized parties can decipher. Pseudonymization is a process that replaces identifiable data with a pseudonym, or a fictional name or identifier. These methods can help protect user data from unauthorized access.
Monitor for bias
Prevent bias in AI algorithms by monitoring datasets for unfairness and seeking stakeholder feedback.8 Organizations can also use techniques such as simulated annealing (SA) to detect potential bias in the system before it is deployed.9 SA is a process of optimization that finds the best solution from a set of potential solutions. By addressing potential biases early, organizations can create more accurate and trustworthy AI systems.8
Incorporate human oversight
When designing AI systems, ensure that they are supervised and monitored by human experts. This can help reduce the risk of errors or misuse and provide a layer of accountability for any decision-making process. Despite the advancements in AI, identifying and addressing potential ethical issues still requires human intervention.8
Organizations can seek out and collaborate with partners who have expertise in the responsible use of AI. This includes partnering with universities, think tanks, and government bodies to ensure that initiatives are consistent with the latest research and regulations.
Marquette University strives to promote responsible AI use through its research and education initiatives. Through its machine learning and artificial intelligence research group at the Department of Electrical and Computer Engineering (EECE), the university is developing technologies that are designed to meet the highest ethical standards and ensure that AI systems are used responsibly.
Marquette's commitment to ethical AI practices has helped raise awareness of this important tech industry issue. The 2021 Ethics of Big Data Symposium, hosted by Department of Computer Science at Marquette University, is just one example of its efforts to educate and advocate for responsible AI use. By taking the lead in this area, Marquette is setting an example for others to follow.
Establish yourself as a professional who leads innovation with an eye on ethics
The integration of AI in business operations increases the need for professionals who understand the importance of ethics in artificial intelligence. Through a combination of real-world knowledge and ethical leadership training, Marquette University’s online MBA prepares you to lead with integrity in today's rapidly changing business world. To learn more about our program, speak to an Admissions Advisor today.
- Retrieved on May 6, 2023, from iapp.org/news/a/privacy-and-responsible-ai/
- Retrieved on May 6, 2023, from lexology.com/library/detail.aspx?g=ef49519e-78f8-4713-9e4a-52bd747938fa
- Retrieved on May 6, 2023, from il.boell.org/en/2022/03/21/whos-blame-when-artificial-intelligence-systems-cause-damage
- Retrieved on May 6, 2023, from gdpr.eu/what-is-gdpr/
- Retrieved on May 6, 2023, from standards.ieee.org/industry-connections/ec/autonomous-systems/
- Retrieved on May 6, 2023, from ainowinstitute.org/our-work
- Retrieved on May 6, 2023, from hbr.org/2022/06/building-transparency-into-ai-projects
- Retrieved on May 6, 2023, from brookings.edu/research/algorithmic-bias-detection-and-mitigation-best-practices-and-policies-to-reduce-consumer-harms/
- Retrieved on May 6, 2023, from cambridge.org/core/journals/knowledge-engineering-review/article/using-pareto-simulated-annealing-to-address-algorithmic-bias-in-machine-learning/B45797B8FFD7BFBBD6F0B2FB549B97FA