Artificial intelligence (AI) and machine learning (ML) have become mainstays in virtually every industry today, from healthcare and finance to government agencies. These and similar technological advances in data analysis, automation, problem-solving, and more are changing so many fields, it can seem difficult to keep up. Thanks to AI and machine learning, businesses and organizations enjoy a variety of benefits related to operational efficiency and data-driven decision-making.

This kind of boundary-pushing technology also brings key ethical considerations. Since artificial intelligence and AI systems are designed to perform tasks like human beings would, it’s important to ensure that AI is used responsibly. Misuse of AI or machine learning technologies could raise numerous issues with potentially widespread effects.

But before AI can be properly used, it must be taught and understood. This blog discusses AI and machine learning ethics and how tech education can prepare AI practitioners for the future. Read on to learn more.

Understanding AI and AI Ethics

Artificial intelligence refers to computer and machine systems that can be trained to learn, identify patterns, and provide solutions to complex problems. Common examples of AI use cases include smartphone virtual assistants, self-driving car navigation algorithms, and generative AI models like ChatGPT.

While the goal of most—if not all—AI applications is to streamline and optimize human productivity, responsible AI usage should remain a priority. When individuals or organizations entrust AI and machine learning systems with access to any kind of data, they need to understand the moral implications behind their use of AI. Doing so means establishing and abiding by specific guidelines to uphold AI ethical standards.

The Current Landscape of AI Ethics

Although AI may make everyday life more convenient, it also poses potential dilemmas for users, AI trainers, educators, and ethicists and policymakers who are tuned into AI technologies. Some of the most pressing challenges include bias in AI and ML algorithms, data privacy concerns, and transparency issues. And as AI spending reaches record levels across industries, these ethical qualms are becoming more consequential than ever.

An increasing number of policies and industry standards have been put in place to address current ethical concerns regarding AI. These standards vary across organizations but most commonly include the following:

• Privacy and data protection
• Accountability
• Fairness and inclusiveness
• Transparency and trust

Companies like IBM, Bosch, Google, and many others publish and update policies about AI ethics pertaining to their unique circumstances, employees, and stakeholders. Thanks to the principles outlined in these policies, AI practitioners and other professionals whose jobs are directly impacted by artificial intelligence can make informed decisions and harness AI properly.

Bias and Fairness in AI Systems

AI systems are created by humans, and sometimes, that means these systems can inherit latent biases or prejudices from their creators. Virtually all types of AI and ML models function via algorithms—sets of directions that instruct models to parse and analyze data and then make calculated decisions based on analysis results.

Algorithmic bias occurs when AI and machine learning models are given skewed data that favors one idea or societal group over another. If these biases persist, then using AI for different business applications—like hiring employees or authorizing loans—may perpetuate social or socioeconomic inequalities.

One of the best ways to combat bias in AI systems is to address the issue in relevant educational institutions. Careful curriculum design, relevant hands-on training, and open classroom discussion about AI bias are just a few practical methods that may help students and professionals alike to detect and mitigate bias.

Privacy and Data Governance

Digital repositories of data are so commonplace today that entire industries like cybersecurity and data governance have been created to keep that data secure. Since AI relies heavily on data to learn, recognize patterns, and make informed decisions, it’s crucial to train AI against compromising private or personal information. Organizations that use AI should always adhere to data privacy protection regulations and only collect as much data as they need.

Students eager to begin an IT or information security career can benefit from learning about the relationship between AI and data privacy in a university degree program. Technology degrees in software development, data analytics, and cybersecurity and information assurance can prepare students to handle sensitive information responsibly as they engage with AI and AI ethics guidelines. To become good stewards of AI systems, students can also internalize AI governance frameworks and data privacy principles such as the following:

• Data encryption
• Network security
• Opt-in data sharing and user consent
• Data intermediaries
• Data minimization
• Access control

Transparency and Accountability

A clear understanding of how AI works is an essential aspect of transparency. Many AI and machine learning systems have often been characterized as “black-box” systems, meaning there is little or no explanation regarding how they make certain decisions. As the complexity of data increases, an AI’s decision-making processes often become more opaque. And the less transparent AI systems are, the more difficult it is for organizations using them to remain trustworthy.

Fortunately, modern tech education can help promote ethical standards of transparency and explainable AI principles. IT degree programs—and the coursework, projects, and assessments that make up these programs—can teach learners about the moral implications of AI in relation to its explainability, interpretability, and reasoning.

Building Tomorrow’s Ethical Tech Leaders

Because artificial intelligence is here to stay, AI practitioners need to understand the ethical and moral considerations of using it. WGU is committed to preparing tech professionals to prioritize AI ethics in their work. This way, they can maximize the benefits of AI, foster public goodwill, minimize bias, and keep private data secure.

WGU’s tech programs—including our master’s degrees in software engineering and computer science—incorporate ethical AI principles throughout their curricula, preparing graduates to address real-world challenges in AI development and implementation. Each of these flexible, online degree programs is designed with input from industry experts and confers job-ready skills that employers value.

Plus, WGU’s competency-based learning model means that you advance through coursework as quickly as you master the material, potentially saving you time and money.

Learn more today.