Modern stakeholders prefer AI-driven solutions. Nonetheless, they expect fair and transparent data handling practices. Let’s explore this topic thoroughly.
Table of Contents
1. Key Ethical Frameworks on Ethical AI
1.1 Transparency and Explainability
2. Interesting Courses on Ethical AI
2.1 Coursera: Ethics of Artificial Intelligence
2.2 The University of Edinburgh: Data Ethics, AI, and Responsible Innovation
3. Challenges and Solutions for Ethical AI
Artificial intelligence is one of the hottest topics in the business world, as it can help top-level managers streamline their business processes easily and efficiently. However, numerous CIOs and tech leaders believe that in the long run, AI must adhere to some fundamental rules and regulations to gain stakeholders’ trust and have integrity while making crucial decisions. In this article, we will explore the idea and key framework of responsible AI and how to overcome challenges for it.
1. Key Ethical Frameworks on Ethical AI
Companies need to create and operate AI responsibly by adhering to a few rules and regulations. Thus, the implementation of an AI ethical framework will provide the company with a better idea when developing an AI system. Here are the essential ethical frameworks that will help you meet the standards:
1.1 Transparency and Explainability
Responsible AI systems tend to be transparent, which enables users to understand how the system works and how to use it to make informed decisions. IT developers are trying their best to create a proper AI model that can explain and interpret its users based on an algorithmic decision-making process.
1.2 Data Privacy and Security
A responsible AI framework prioritizes protecting users’ data and respects privacy rights. Companies should adopt robust data protection measures that include securing data storage, following the General Data Protection Regulations (GDPR), and anonymizing the system when needed.
1.3 Continuous Monitoring
AI systems need to be continuously monitored while operating to ensure that tasks are performed properly. Monitoring includes inspecting the performance of the algorithms against the key points with the help of smart metrics and key performance indicators (KPIs). These will help in indicating the limitations and will help the CIOs come up with solutions.
1.4 Educating and Training
The principles alone are not enough to achieve ethical AI in the company; training and educating the employees who will be using AI are equally important. These initiatives are generally taken by IT top managers and HRs to identify employees’ concerns and areas of risk and advise them to take up necessary courses related to AI ethics.
2. Interesting Courses on Ethical AI
Several companies and educational institutes have come up with courses and training that will help you and your employees understand AI responsibilities. Here are a few of them:
2.1 Coursera: Ethics of Artificial Intelligence
The course allows you and your team members to understand the ethical, cultural, and social effects of AI and encounter discussions with AI practitioners to create awareness. You and your team will also get insights to analyze the ethical AI framework with the help of examples and case studies.
2.2 The University of Edinburgh: Data Ethics, AI, and Responsible Innovation
The course helps in examining the political, legal, social, and ethical problems with data-driven techniques, ML, and AI systems. You will find numerous case studies on AI bias, data reuse, fair use of AI and ML systems, data protection, data privacy, and many more, and they will provide you with in-depth solutions.
2.3 LSE: Ethics of AI
This is a three-week online course on AI ethics that helps in understanding and examining AI technology’s ethical problems and solutions. Through this course, you and your team professionals can apply moral ideas to actual situations to understand and resolve issues in AI ethics, like transparency, inequality, etc.
3. Challenges and Solutions for Ethical AI
The business world is gradually relying on artificial intelligence, and CIOs and tech leaders might face ethical challenges in fostering AI in their respective companies. One of the main concerns will be the bias of AI algorithms, which can influence incorrect decisions, leading to unfair treatment for stakeholders. However, there are solutions to these challenges that can help you and your team use AI systems appropriately in the future. Let’s take a look below:
3.1 Bias and Discrimination
Artificial intelligence systems, when merged with a computer program can benefit businesses, but it can be risky if the AI system is not well-trained. For example, the Amazon recruiting engine is an AI that analyzes the resumes of job applicants when applying for Amazon and furthers the company process with interviews and selection. However, the Amazon algorithm turned out to be biased against women in the recruiting process. The HR department of Amazon witnessed that more men were applying for jobs and getting selected for further rounds. When Amazon studied the algorithm, they found that it rejected resumes containing words like “women”, “female”, and “all-women colleagues”. Amazon had to discard the AI algorithm and did not use it to evaluate candidates for the recruitment process.
Solution: The AI bias issue can be resolved by data cleansing, using fair algorithms, proper feedback, and human intervention. CIOs and tech leaders can implement any of these methods and test and evaluate them properly before presenting them to the real world.
3.2 Stakeholders’ Trust
In the large-scale automated business world, there will be times when stakeholders may be indirectly and directly affected by the use of AI, such as in the development and operation of business processes, safety measures, and deployment.
Solution: Building trust among the stakeholders is crucial by informing them about the data the company has while also explaining the process and the requirements for protecting data. This will help both the tech leaders and stakeholders stay on the same page and build trust among each other.
Till now, we have understood how to mitigate and address challenges. Here is a real-world case study on Microsoft, which has successfully implemented a responsible AI department in their office.
4. Case Study – Microsoft
In 2019, Microsoft created The Office of Responsible AI to resolve issues like designing chatbots, the input of proper data, and much more, and develop and deploy AI systems properly into their computer programs. Microsoft’s Chief Responsible AI Officer, Natasha Crampton, said, “In June 2022, we decided to publish the Responsible AI standard. We don’t normally publish our internal standards to the general public, but we believe it is important to share what we’ve learned in this context and help our customers and partners navigate through what can sometimes be new terrain for them, as much as it is for us.” Currently, Microsoft is working on building a concrete AI ethics practice ecosystem that will help the company make more tools and techniques without any errors.
In the fast-moving environment, AI is helping various industries, but the future is still unknown. The tech leaders believe that if AI is used ethically, it can make positive changes in business. Let’s take a glance at what the future of responsible AI will look like.
5. Future Scope
AI ethics will continue to grow faster in the future, with a vision that it will benefit the business world. Companies are currently implementing their own set of rules and regulations for AI and putting them into immediate action. The only concern is that the AI ethical framework may not apply to all industries, and SMBs and SMEs may lack the essential resources to develop their framework. However, it is believed that with the help of training and tools, the AI and ML algorithms can be molded to improve persisting issues, like cyber security and safety issues related to AI systems.
What Lies Ahead?
Artificial intelligence has become a powerful and widespread model that is used by every industry, but it also accompanies risks that can lead to reputational damages and the loss of customers’ faith in your business. Thus, to safeguard these risks, your company can implement an AI-responsible framework for shaping a fair, positive, and better future for your business.
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