<p data-block-key="tvdx8">Generative AI systems are based on Machine Learning (ML) models. These ML models are complex and use sophisticated algorithms. Two popular ones we have already mentioned are ChatGPT and DALL-E.</p><p data-block-key="f87lg">Let’s individually understand both of these generative models.</p><h3 data-block-key="7jsq">1. ChatGPT</h3><p data-block-key="d9j14">ChatGPT stands for Generative Pretrained Transformer. It is essentially a free chatbot that has been developed by OpenAI. Users can ask any question to it, and it will generate relevant answers. It allows users to engage in human-like conversations with it. Not only that, it helps users write emails, essays, and even code.</p><p data-block-key="bdto0">Today, it has become one of the most used AI chatbots. It has largely influenced productivity among employees. The technology is being utilized by users across the world for creating all kinds of content – from essays to poems to marketing copies.</p><h3 data-block-key="52b7o">2. DALL-E</h3><p data-block-key="466jr">DALL-E has also been developed by OpenAI. It is an AI system that can create realistic images and art based on a description in natural language. It is a text-to-image model that can create detailed images with accurate faces and texts and can even replicate human hands. It uses an AI technology known as ‘transformer neural network’ to do so.</p><p data-block-key="d4juh">Both these technologies show that generative AI has the potential to transform how industries work and perform their jobs. While generative AI offers many benefits to businesses, especially in improving their internal processes, the importance of AI ethics should not be forgotten.</p><p data-block-key="9ga96">Why are AI ethics important? And how do they apply to generative AI? Let’s take a look.</p>

Understanding Generative AI

<h2 data-block-key="wwogz">Understanding Generative AI</h2>

<p data-block-key="tvdx8">In simple terms, AI ethics means developing, deploying, and using Artificial Intelligence in a manner that is legally and ethically correct. The goal of AI ethics is to make sure that AI systems are designed in a manner that supports human values and human rights.</p><p data-block-key="1rapj">Responsible AI implies that AI and ethics should work jointly. When AI is used responsibly, it mitigates the chances of bias, risks to security, and discrimination. The ultimate objective of incorporating ethics in AI is to increase transparency and reduce bias.</p><p data-block-key="9euj4">Responsible AI recognizes that there should be a balance between technology and individual well-being. It demands a proactive approach from businesses. It encourages businesses to identify and mitigate potential risks posed by AI.</p><p data-block-key="4863r">It also demands that businesses create and deploy AI algorithms that are transparent and fair. They must benefit society without compromising ethics. What are these ethical considerations that businesses must keep in mind when building AI systems?</p><p data-block-key="fjovu">Let us understand the connection between AI and ethics and why businesses should take the concept of AI ethics seriously.</p>

What are AI Ethics?

<p data-block-key="wwogz"><strong>What are AI Ethics?</strong></p>

<p data-block-key="p9szq">While incorporating AI in business processes hugely benefits organizations, fully understanding how these systems work is challenging. Businesses need to understand how these systems make their decisions or predictions. They must evaluate if their AI model is transparent.</p><p data-block-key="1anl2">Because if the functioning of AI itself needs to be properly understood, how can businesses place their confidence in it or claim that their AI model is accurate? This can be problematic. For example, AI is used in the Healthcare industry to make predictions and detect diseases. A wrong decision made by AI can cause significant consequences to patients and the industry.</p><p data-block-key="fk7jk">Let us review key concerns that have led to discussions around AI ethics:</p><h3 data-block-key="e5icc">a. Presence of Bias</h3><p data-block-key="sa1g">AI systems are designed by humans. They are trained on data and rely on it to make decisions. Since these models learn from data, they are likely to be biased against data that is poorly fed to them. We have already mentioned an example of how Amazon’s AI downgraded resumes featuring women. To remove this bias, businesses must continuously monitor the data being used to train these AI models.</p><p data-block-key="comjk">&nbsp;</p><h3 data-block-key="83pg8"><strong>b. Lack of Transparency</strong></h3><p data-block-key="1tsl9">AI models often lack transparency. It can become difficult to understand how they make decisions or predictions. This becomes a challenge in cases where AI-based decisions have a direct impact on individuals. In such cases, who should be held accountable and responsible? AI ethics encourages businesses to design transparent AI models. We will discuss how later on.</p><h3 data-block-key="2ukbk"><strong>c. Privacy Concerns</strong></h3><p data-block-key="5dnmk">AI models collect and process personal information and data to learn. A large part of this information comes from users. However, not all users are aware that their information and data are being used to make decisions. For instance, AI models are used for facial recognition technology in public places. But is there a process to check if those individuals have consented to their data being collected? Businesses must implement AI principles that follow privacy regulations and data protection measures.</p><h3 data-block-key="9mr80"><strong>d. Absence of Control</strong></h3><p data-block-key="dv3ie">There is a fair chance that AI systems can operate autonomously since many AI models are also self-learning. This can lead to unforeseen consequences. Ethics in AI are important for businesses to maintain a check and control over AI systems.</p><h3 data-block-key="fm6ht"><strong>e. Impact on Environment</strong></h3><p data-block-key="2ud00">Businesses that employ AI/ML development services often overlook the fact that some large AI models need a significant amount of energy to train data. AI ethics calls for businesses to look for energy-efficient and environmentally safe AI development practices.</p>

Why are Ethics in AI Important?

<h2 data-block-key="owohu">Why are Ethics in AI Important?</h2>

<p data-block-key="p9szq">There is hardly anything generative AI cannot do. From writing college essays to poems to lines of code and more, generative AI is used for multiple purposes to create all types of content. While, on the one hand, AI helps digital transformation by improving business operations, it is stirring concerns regarding ethics, such as misinformation, plagiarism, copyright infringement, legal, and several other concerns on the other hand.</p>

The need for AI ethics in generative AI

<p data-block-key="owohu">The need for AI ethics in generative AI</p>

<h3 data-block-key="8qd9b"><strong>1. Producing Harmful Content</strong></h3><p data-block-key="e4rv7">Generative AI has the ability to produce human-like, relevant content. This definitely has a positive impact on a business’s productivity. At the same time, it can also have a negative impact if not applied properly.</p><p data-block-key="21m8m">There are tools like Deepfakes that can manipulate false images, videos, texts, and speech. People may use it to fulfill their personal or political agenda or to propagate hate speech. You might be aware of the trend where people manipulate the facial and voice features of popular personalities or individuals using AI.</p><p data-block-key="eutcu">Now imagine if this technology were to be accessed by someone with wrong or malicious intent. For instance, a kidnapper clones the voice of a young child to demand ransom from their guardians. Is there a way to distinguish a real voice from a fake one? As of now, there isn’t.</p><p data-block-key="9cgfm">As these generative AI tools keep getting sophisticated, there is an urgent need for businesses to intervene. Businesses need to align their AI models with ethical practices. They should include AI ethics in the initial model development stage itself.</p><h3 data-block-key="7ush8"><strong>2. Copyright Infringement</strong></h3><p data-block-key="anac4">Generative AI models need to be trained on large amounts of data. Where is this data taken from? From various text and image sources across the internet. Since these AI models utilize all available data to generate content, the original data source becomes untraceable.</p><p data-block-key="5sp6n">What if a company uses the generative AI model to write lines of code, and the AI model that generates them uses another company’s product to do so? This can pose a risk to businesses, especially financial institutions that handle sensitive data.</p><p data-block-key="au50k">On top of that, it infringes on copyright and intellectual property of companies. Businesses that blindly rely on data generated by AI models can face legal and reputational risks. Ethics in AI urges businesses to adopt a checking system that determines if the data output given by AI models is right or not.</p><h3 data-block-key="4t12p"><strong>3. Compromising Data Privacy</strong></h3><p data-block-key="sf7d">The data used to train AI models might contain information that is capable of identifying the user. This information is known as Personally Identifiable Information (PII). This happens when data like name, address, email address, etc., is input into the AI model while training.</p><p data-block-key="4qku4">Extracting this data is also not that difficult; it just takes a simple text prompt. If the user data is compromised, it can easily lead to identity theft or misuse.</p><p data-block-key="2o2l8">Developers and businesses must check whether their systems are secure and follow data privacy guidelines. Moreover, they must also ensure that PII is not embedded into these AI models while training.</p><h3 data-block-key="admd1"><strong>4. Disclosing Sensitive Information</strong></h3><p data-block-key="g1r1">Generative AI has democratized Artificial Intelligence. This means users can easily access and use AI capabilities without needing any technical skills or knowledge. For example, ChatGPT can be used by any individual.</p><p data-block-key="crpci">There is a chance that users might overlook data security to gain access to AI models. They might unknowingly disclose sensitive information about themselves or someone else while using these AI models to obtain data.</p><p data-block-key="4stob">For instance, an employee uploads a source code of a particular software. Or a brand discloses its product marketing strategy to a third party. This hugely increases the risk of sensitive information being leaked.</p><p data-block-key="di0d4">As a result, businesses have to deal with the financial and legal consequences. To avoid that, businesses must enforce clear ethics in AI guidelines and communication for sharing sensitive information.</p><h3 data-block-key="7gqjt"><strong>5. Creating Bias</strong></h3><p data-block-key="2660j">As we have discussed above, AI models are trained by humans. If the training data is biased, the output generated by the AI model will also be biased. Businesses that hire AI developer must make sure their team has diverse subject matter experts who check bias during the training phase of AI models.</p><h3 data-block-key="261go"><strong>6. Data Governance</strong></h3><p data-block-key="9rvj">Generative AI models work by accessing large amounts of data. This data may not be governed properly. Its origin cannot be traced, or it may be biased or used without consent. How can the accuracy of data be trusted?</p><p data-block-key="1vl3t">Businesses should label this data as ‘AI-generated data’ so that they can monitor where this data is applied. Or else they must implement strict data security and integrity measures so that the data they use is not biased or questionable.</p><h3 data-block-key="7e7oo"><strong>7. Threat to Workforce</strong></h3><p data-block-key="437k4">AI can perform almost all tasks experienced humans can, with higher efficiency. This includes coding, writing, analysis, creating content, illustrations, and more.</p><p data-block-key="d9q3f">As more and more businesses have started to use generative AI in their processes, a new issue has come to light. Are AI systems capable of replacing humans?</p><p data-block-key="4d3gn">While generative AI models are still being tested, businesses should understand they are here to stay. Businesses must prepare their workforce to embrace new roles created by generative AI. They must offer training to their employees to develop skills such as prompt engineering.<br>Businesses must create a balance between AI and ethics to reduce threats posed to their workforce by generative AI and adopt it at an organizational level.</p><h3 data-block-key="2mndt"><strong>8. Lack of Trust</strong></h3><p data-block-key="38nes">Most generative AI models generate data based on a combination of various sources. The chance that the data they generate is based on probability cannot be overlooked. The reason behind this is simple: Generative AI works by associating one or more similar data sources with another.</p><p data-block-key="20mua">This brings focus toward the question, is data generated by AI models trustworthy? And is it right to accept this data at face value? Especially in cases where data or answers generated by AI models can impact human life, like in the case of the healthcare sector.</p><p data-block-key="5qo3g">Instead of accepting data generated by the systems as it is, businesses should look into why a particular AI model arrived at that response and what factors are responsible for it generating particular data.</p>

Generative AI and Ethics: Major concerns

<h2 data-block-key="s7rvi">Generative AI and Ethics: Major concerns</h2>

<p data-block-key="h0h3a">The concept of responsible AI includes the use of specific approaches by businesses with the goal of removing AI bias. Businesses continuously test AI to determine bias. If any bias is detected, the information is eliminated. But on what basis do businesses determine bias?</p><p data-block-key="fkm2q">There are a few principles that act as a framework. Businesses can use these AI principles to make sure that AI systems are beneficial to their organization as well as to society. Let’s explore those.</p><h3 data-block-key="amu80"><strong>1. Accountability</strong></h3><p data-block-key="d4bqv">The first pillar of AI ethics is accountability. While AI can be extremely useful for speeding up internal processes through automation, it is important to consider its reliability and accountability. Take, for example, self-driving cars that are trained using AI-based Machine Learning algorithms.</p><p data-block-key="efvrn">These cars are presumed to know what the right driving decision for the human driver is. But, the ‘right’ driving decisions will depend on the engineers and developers who designed the model. So, how accurate will these systems be? And how much trust can be put into them?</p><p data-block-key="filuv">Let’s look at another example. AI is used in lending and loan management applications. To make a successful lending decision, the model is taught to analyze various parameters like loan amount, installment period, interest rate, etc.</p><p data-block-key="2edcm">But once again, there is no way to determine if these models operate fairly. It is the engineers and developers who decide what loan amount the ML model would consider fair and reasonable for a particular individual.</p><p data-block-key="ekcds">This creates a bias in the system. A loan amount approved for one individual may not be approved for another individual with a similar profile. To avoid this from happening, businesses must think about implementing an AI ethics committee.</p><p data-block-key="burho">This committee can review policy decisions and conduct audits to make sure that the AI model is accountable and unbiased.</p><h3 data-block-key="2o424"><strong>2. Reliability</strong></h3><p data-block-key="1mgob">Along with accountability, reliability is another AI and ethics principle. Many businesses do not have a uniform source of data. They use data from various sources such as data warehouses, data stored in the cloud, etc. On top of that, data sources keep changing constantly.</p><p data-block-key="3a5bi">So, if a business does not have uniform data, how can it make profitable data-driven decisions or upgrade its processes? The answer is, by ensuring consistency–both in data and AI/ML models. How can businesses achieve that?</p><p data-block-key="3kj2k">Businesses can begin by standardizing their data collection process. This can be done by building AI systems that can organize data collected by ML models. Besides that, businesses should make sure that they use the latest tools and technologies to collect and process data.</p><p data-block-key="78aqk">Another thing businesses must do is monitor their AI systems and develop reliable algorithms for processing data. This will help businesses optimize their testing and validation process. This, in turn, will help them generate accurate dashboards for data visualization.</p><h3 data-block-key="42r3u"><strong>3. Transparency</strong></h3><p data-block-key="d84s5">AI transparency ensures that the AI model is explainable. What exactly does explainable mean? Let us understand with an example.<br>Imagine you own a banking or healthcare software development business for which you have built an AI/ML model. And that there are certain features that you have incorporated into this model.</p><p data-block-key="9bt9o">Now, for customers to use these features, they must be able to understand how that particular feature is beneficial for them. Businesses must be able to justify to their customers why a particular ML feature exists and how it benefits them.</p><p data-block-key="9rc63">To ensure AI transparency, businesses must monitor statistical AI performance and metrics. It is also important to maintain transparency in AI models to ascertain that there is no bias and that the AI/ML technology is creating useful results.</p><h3 data-block-key="9be6v"><strong>4. Security</strong></h3><p data-block-key="fihol">Technological security is a major concern. Since AI systems are prone to threats and cyberattacks, it is necessary to protect them. Businesses should look into potential risks and how they can impact AI technology. They must consider implementing measures like encryption to prevent the threat of attack.</p><p data-block-key="88t6l"><a href="https://arxiv.org/abs/1707.08945">A paper has been published</a> by a group of researchers from the University of Washington, Stony Brook University, the University of California Berkeley, and the University of Michigan. The paper discusses how visual grouping algorithms of ML models can be tricked. Let’s understand this study in a nutshell.</p><p data-block-key="oqp2">There was an AI-based computer vision model that was designed to detect stop signs. However, when someone put up even a tiny sticker on the stop sign, the system could be fooled.</p><p data-block-key="4a4jd">It is interesting to note that this sticker wasn’t even distinguishable by the human eye. This means that even a small-scale human error or interference with AI models can greatly impact safety and security.</p><p data-block-key="beich">Businesses must critically evaluate their existing AI systems, understand risks, and undertake measures to reduce those risks. The AI models they rely on should be dependable and secure.</p><h3 data-block-key="culj6"><strong>5. Privacy</strong></h3><p data-block-key="2ekd9">AI collects a large volume of data. There need to be clear, established guidelines on how this data is used. Protecting customer data should be a top priority among businesses. Especially when it comes to implementing AI in banking or in industries like healthcare that collect sensitive data.</p><p data-block-key="8orq7">Both businesses and individuals are relying on Cloud technology to store and share their personal data. Consequently, they should also pay attention to relevant privacy policies.</p><p data-block-key="41p8v">Besides following government regulations, every business should implement data safety and privacy at an organizational level. One way to do this is by generalizing data so that the AI models do not store individual customer records.</p><p data-block-key="bomjk">Businesses should build secure AI models to build customer trust and protect their data infrastructure from cyber attacks.</p><p data-block-key="csmd4">Even after considering these five AI principles, businesses must understand that AI and ethics are evolving concepts. Implementing AI ethics means continually improving and upgrading AI as the technology keeps changing.</p><p data-block-key="bn3ib">However, what you can do, as a business, is keep the following things in mind to promote AI ethics when developing generative AI models.</p><ul><li data-block-key="arh83">User-Centric Design: Before building AI models, survey your end-users. Consider their needs and values. Businesses can do it by researching their end audience and through continuous testing.</li><li data-block-key="5a3ks">Transparency: Your AI system must be transparent. You should be able to justify how your AI model makes decisions and what data it uses to make those decisions. This is necessary for you to build trust among your customers so that they are encouraged to use your system.</li><li data-block-key="c8o18">Unbiased: AI models should be developed to be unbiased toward all users regardless of their gender, ethnicity, race, etc. Businesses can achieve this by using diverse data sets and setting up committees to keep bias in check.</li></ul>

The-Fundamental-Pillars-of-Responsible

The Fundamental Pillars of Responsible Generative AI Principles

<h2 data-block-key="edgir">The Fundamental Pillars of Responsible Generative AI Principles</h2>

<p data-block-key="9pg4h">It is very clear that AI is bringing about several transformations across businesses around the world. Through AI-based Machine Learning, machines now have the ability to process data in the same way as humans. And this technology is rapidly developing.</p><p data-block-key="c1t68">AI is now being used to diagnose diseases, build self-driving cars, and in facial recognition and biometric technologies, to name a few. As this technology progresses, the responsibility on the part of businesses to ensure safe and ethical AI practices grows, too.</p><p data-block-key="807vf">Businesses creating AI models need to understand their obligations to society. This includes understanding the limitations of AI technology. As AI interacts more and more with society, issues of privacy, transparency, and accountability are raised.</p><p data-block-key="9tkmf">Before integrating modern AI systems into society, businesses must understand how these systems work, i.e., they should be able to justify how they make decisions. Especially in cases where AI is used in safety-critical conditions, like in the case of self-driving cars.</p><p data-block-key="27u6k">Businesses need to understand the risks posed by AI and measures that can be taken to combat them. For that, businesses must start by educating themselves on what AI is, how ethics in AI influence development, and how it can be leveraged to maximize benefits and remove bias.</p><p data-block-key="erobq">At Quixom, we combine the capabilities offered by AI with responsible business practices. Our AI/ML development services help enterprises <a href="https://www.quixom.com/enterprise-oriented-solutions-to-unlock-growth/">build responsible AI that ensures transparency and adapts to governance strategies</a>. Our team works side by side with you to design AI systems that are compliant and reliable.</p>

Integrate AI Ethics Into Your Generative Models

<h2 data-block-key="094o3">Integrate AI Ethics Into Your Generative Models</h2>

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<p data-block-key="xr87y">Law graduate turned storyteller, I am a tech enthusiast who explores the world of marketing through creative writing. I help brands create engaging stories to connect with their target audience.</p>

Law graduate turned storyteller, I am a tech enthusiast who explores the world of marketing through creative writing. I help brands create engaging stories to connect with their target audience.

Prajakta Joshi

<p data-block-key="x28z2">When we reflect on all the technological advancements made in the past decade, the role of Artificial Intelligence (AI) cannot be overlooked. From self-driving cars to human-like robots, the role played by AI and AI-based algorithms is prominent.</p><p data-block-key="6abha">Many businesses rely on AI models to automate repetitive tasks, speed up workflows and processes, and mitigate human errors. It has become impossible to imagine a world without AI. AI is easily able to perform complex human tasks with significant accuracy.</p><p data-block-key="g2vf">If you think about it, a few years ago, you wouldn’t have imagined that a machine could create art, write poetry, or understand complex data like legal documents. However, it is a reality today, thanks to the discovery of generative AI.</p><p data-block-key="7pvvg">Generative AI is an emerging AI technology, and it is taking the world by storm. It is a sophisticated branch of AI that empowers machines to generate new content by using and refining existing audio, texts, images, etc.</p><p data-block-key="fv5m9">Its prime examples include ChatGPT and DALL-E, two popular AI models that operate on large amounts of training data. AI technology, no doubt, is one of the most impactful technology.</p><p data-block-key="74dn1">The advances made in AI-based language models have stirred a mass interest in the potential AI holds in achieving transformations across almost all industries. OpenAI’s ChatGPT is at the center stage, determining how generative AI can impact businesses in every corner of the world.</p><p data-block-key="6thk9">According to a report on <a href="https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html">Artificial Intelligence statistics published by PwC</a>, including generative AI in business processes can potentially increase global GDP by 14% in 2030.</p><p data-block-key="o9io">However, with numerous AI advantages come its own sets of challenges. Just recently, OpenAI’s ChatGPT was charged with a defamation suit for hallucinating court cases. An attorney at Levidow &amp; Oberman had used the AI chatbot to find prior cases to support his current case.</p><p data-block-key="ebkon">At least six of the cases cited by ChatGPT did not exist. Not only that, the cases generated by the AI chatbot contained false names, docket numbers, and even international citations. In another instance, Amazon’s recruitment bot came under the spotlight for being gender-biased against women.</p><p data-block-key="1ahvb">At such a time, the question arises: Is generative AI technology being used responsibly? Or is there a chance it might pose business risks? And if so, should businesses consider ethics in AI?</p><p data-block-key="ddarg">A key step to building superior AI technology must begin with calculating risks. Generative AI technology should be used responsibly to avoid risks to businesses. AI ethics plays an important role.</p><p data-block-key="5ar5d">In this blog, we will talk about what generative AI is, the ethical risks associated with its implementation, and the best practices businesses should follow when applying generative AI ethics.</p>