In the current era, many marketing tools are powered by AI, which poses a challenge for marketers due to the potential for unintentional algorithmic bias that can impact platforms. These biases can be difficult to detect and resolve, as they can be programmed into the AI without the knowledge of data science teams.

For marketers, it is crucial to be aware of and account for any biases that may exist within the algorithms used for advertising, whether they are developed in-house or purchased from vendors. It is important to take concrete steps to minimize bias in these algorithms, regardless of whether they are proprietary AI or third-party solutions.

Machine learning, in particular, has already been successfully integrated into various marketing solutions such as hyper-segmentation, dynamic creative, inventory quality filtering, dynamic sites, and landing pages. However, several factors can impede the success of these algorithms.

What is AI failure and why it happens?

Before we delve deep into AI bias and its consequences for marketers let us understand what is AI failure and why it occurs.

An artificial intelligence (AI) system producing unexpected, erroneous, or substandard results that can diverge from its intended goal is referred to as an instance of AI failure. Hence, there are various factors that contribute to AI failure, including bad data quality, biased algorithms, faulty programming, problematic hardware, and insufficient testing.

Because AI failure can have serious repercussions like financial losses, reputational harm, safety issues, or legal liability, we must be aware of it and its effects. Hence, when it comes to creating and deploying AI systems, marketers need to take crucial steps and they should prevent AI failure. It can be beneficial to collaborate with design and deployment teams in order to develop trustworthy, dependable, and transparent AI products that need to be regularly monitored and evaluated for performance.

There are many different ways that AI might fail, thus it’s important to recognize these modes in order to stop or lessen their consequences. Here are a few typical AI failure scenarios:

• Bias: The quality of AI models depends on the data they are trained on. Biased training data will result in biased behavior from the AI system, which will produce unfair and discriminatory results. There are several different types of prejudice, including algorithmic bias, dataset bias, and cognitive bias.
• Underfitting and Overfitting: Overfitting occurs when an AI model is overly sophisticated and has “memorized” the training set of data. The model consequently performs badly with fresh data. On the other hand, underfitting happens when the AI model is too straightforward and fails to reflect the complexity of data which leads to less accuracy.
• Data Poisoning: Data poisoning is the manipulation of training data by an attacker to skew the predictions made by an AI model. This kind of attack might be difficult to spot and have serious repercussions, such as inaccurate predictions or poor decisions.
• Adversarial attacks: Adversarial attacks entail the introduction of carefully constructed inputs into the AI model by the attacker in an effort to deceive it into producing false predictions. This kind of attack might be difficult to identify and have significant security ramifications.
• Explainability: Explainability is the capacity to comprehend how a certain prediction or judgement made by an AI model was produced. The AI model’s acceptance and adoption may be constrained by a lack of explainability, which might breed suspicion and skepticism regarding its judgments.

So identifying and addressing these failure modes is very critical for ensuring the efficacy of the AI systems and how reliable they are. So, it is very important to conduct thorough testing, validation, and monitoring of the AI systems to minimize the risk of failure. So, now we can understand what is bias in AI and how it is impacting the organization and marketing procedures. What are the things we need to be careful about?

What is bias in AI?

Bias in AI means the systematic errors that can happen when artificial intelligence systems are being developed or deployed. A bias can occur when the data that is being used for the training of an AI model is not a representation of the real world and it leads to the model making inaccurate predictions and decisions.

For example, if the AI system is trained on data that is biased towards one race, gender, and socioeconomic group then the system may be less accurate in making predictions or decisions for the other groups. This causes unfair or discriminatory outcomes.

Bias can also happen during the design of AI algorithms like using certain features that are quite relevant to one group as compared to another or it can be relying on assumptions that are not valid for all the groups. It is vital to address the bias in AI to ensure that these systems are fair, transparent, and accountable. It can include taking steps like improving data collection methods, using datasets that are diverse, and testing the AI systems for bias before deploying them.

Hence, we can now comprehend what bias in AI is and how it affects organizational and marketing practices. What should we exercise caution around?

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Why Advertisers should be careful about AI bias?

AI bias should be avoided by advertisers because it can produce unfair and biased results that harm the brand’s trust and reputation. When AI models are trained on biased data, bias can arise, which can result in unreliable predictions or judgments for particular groups.

​​An AI system, for instance, may not effectively forecast or make judgments for other groups if it is trained on data that is skewed towards one race, gender, or socioeconomic group, which could result in prejudice. This may damage the brand’s reputation and generate unfavorable press, which may result in diminished sales and clientele. The biases in the algorithms must be used for advertising by marketers since they must be inherited. You must devise specific measures to guarantee that there is little bias in the algorithms we utilize, whether it is your own AI or a vendor-provided AI solution.

AI particularly machine learning helps in enhancing a wide range of marketing solutions like hyper-segmentation, dynamic creative, inventory quality filtering, dynamic sites, and landing pages, but there are many other things that can get in the way of making an algorithm successful.

In addition, legal implications may arise if the AI system is found to be discriminatory, potentially resulting in lawsuits and fines. Therefore, it is essential for advertisers to be vigilant about AI bias and take steps to mitigate it, such as using diverse and representative datasets, testing for bias during development, and monitoring for bias after deployment.

Being careful about AI Incidents

By being careful and proactive in addressing AI bias, advertisers can create more fair, inclusive, and effective advertising campaigns that promote brand trust and loyalty.  Artificial intelligence (AI) has become an essential tool for driving marketing effectiveness in today’s digital landscape. Data is generated from every aspect of a business, from customer engagement to website interactions, and leveraging the insights and intelligence from this data can be achieved through the power of AI and machine learning.

The primary goal of AI technology is to provide more intelligence about customers and the business. However, trust is crucial in this exchange, and marketers and advertisers must be aware of AI bias and its potential impact on their ability to reach the right audience. Therefore, it’s important to educate oneself on responsible and explainable AI, which can help mitigate bias. To that end, let’s explore the various types of AI incidents that can occur.

1. AI Incidents:

According to how transformational and affordable AI can be for businesses and markets, many firms are feeling the pressure to adopt it and worry that they won’t be able to catch up if they wait. This is a fair concern. Principal Scientist at bnh.ai and adjunct professor at the George Washington University School of Business, Patrick Hall, issued a warning: if the risk is not properly assessed, as we did with earlier generations of technology, such as railroads and nuclear power, you will undoubtedly invite trouble.

Patrick said that a social media chatbot that was tricked to train with toxic language and a data poisoning attract that happened in 2016 with another similar incident that occurred in 2021 are examples of past AI incidents or failures that are very useful to understand and deal with as they come up. So, we must improve and draw lessons from the past.

The aforementioned example is just one among several incidents, and while it may be less significant, there are more alarming cases where innocent individuals have been wrongfully arrested due to inaccurate facial recognition AI. The Partnership on AI maintains a database of over 1,200 public reports on AI incidents, and journalists are also beginning to scrutinize this issue more closely.

While such incidents are still relatively uncommon, they are not infrequent enough to be dismissed as insignificant. As a brand, it’s crucial to be proactive in preventing potential brand reputational issues that may arise from having a discriminatory or unsafe AI system, as these incidents can be damaging.

2. Some common AI failure modes

Algorithmic discrimination is the most frequent AI occurrence. any time a rule-based system, whether artificial intelligence (AI), machine learning (ML), or another, fails to provide particular groups of individuals with the desired results. In order to prevent bias, people must always be kept informed.

For advertisers and marketers, bias in AI might have unintended consequences like not reaching the right audience and serving the incorrect ads to a particular group. You should consider whether you have reached the intended target or whether you have ignored a certain group of people as a result of which you may have missed some significant sales if your AI has a bias and is picking your audience. So, you are wasting resources and harming the brand’s reputation at the same time.

One of the ways in which AI systems can fail is a lack of accountability and transparency. For instance, several years ago, there were reports on social media about Google search results displaying offensive and inaccurate predictions. In response, Google introduced a reporting mechanism for inappropriate predictions. This feature was deemed essential by Patrick, who believes that other companies should incorporate similar accountability measures into their AI technologies to minimize risks.

Offering consumers the ability to request that automatic decisions not be made about them is a simple yet effective way to mitigate these risks. At Quantcast, we use live data and an AI engine to create audiences through a unified platform called the intelligent audience platform. Ara, our system, undergoes various processes, including peer reviews by machine learning experts and academic rigor.

Additionally, Patrick emphasized the importance of maximum transparency in AI systems. Interpretable and explainable AI systems empower individuals to take action based on their insights. When systems are transparent, it enables humans to review, debug, and govern the system’s behavior.

Therefore, companies must maintain an inventory of all their AI systems, checking them regularly and ensuring that they behave appropriately. Basic security measures, such as bug bounties and red teaming, can make a significant difference. Documentation of AI and ML systems is also important, and a troubleshooting user manual should be provided. Lastly, Patrick recommends having an AI incident response plan and participating in nascent AI security efforts to learn from past mistakes.

How to assess the accountability of AI systems to avoid the bias in AI?

To avoid AI incidents, companies must assess their accountability, and Patrick has outlined seven key points to consider:

1. Fairness: Is there any bias in the model’s decisions across different groups? Are efforts being made to address and resolve these biases?
2. Transparency: Can the decision-making process of the model be explained?
3. Negligence: Is the AI system reliable and safe?
4. Privacy: Is the model complying with privacy policies and regulations?
5. Agency: Is the AI system making unauthorized decisions on behalf of the organization?
6. Security: Have applicable security standards been incorporated into the model, and can any breaches be detected?
7. Third Parties: Is the AI system relying on third-party tools, services, or personnel, and are they addressing these concerns?

Discriminatory bias is the most likely cause of AI malfunction, and thus business managers must ensure their technical managers take steps to identify and rectify these problems. It’s crucial to have human oversight to continuously monitor the AI, as humans are both part of the problem and the solution. Therefore, Patrick recommends that AI systems be thoroughly documented to allow for executive oversight.

Tools that Limit AI Bias

Let us have a look at some tools that will make your system smooth by limiting artificial intelligence biases.

1. ABM AI Fairness 360

The “AI Fearness360 and AIF360” toolset, developed by IBM Research, is a collection of open-source, all-inclusive measurements to confirm unintentional bias in databases and machine learning models. These instruments make up a group of cutting-edge algorithms that can lessen AI bias. The original AIF 360 software, which was a Python library, included nine distinct techniques to reduce unintentional bias. In addition to a collection of tools, the AIF 360 package includes an interactive experience that offers a brief overview of the package’s ideas and potential applications. This interactive experience can be used to determine which measures and algorithms are most suitable for a particular situation.

Also, they were created to be open source to promote the addition of new measurements and methods to the package by scholars from across the world. The package’s development crew was varied in terms of ethnicity, level of scientific knowledge, sexual orientation, years of experience, and a variety of other traits.

2. Fairlearn

Developers and data scientists may evaluate and enhance equity in their AI systems with the help of an open-source toolbox offered by Microsoft. Bias reduction algorithms and an interactive console are the two parts of Fairlearn. The project also aims to incorporate instructional materials on organizational and technical procedures to lessen AI bias, as well as a Python Library to evaluate and enhance the equity of AI.

The Fairlearn toolkit was created with the understanding that there are several complicated causes of bias, some of which are social and others of which are technical, making fairness in AI systems a technically societal dilemma. In order to enable the entire community to get involved and evaluate bias losses, assess the impacts of policies that reduce bias, and then tailor them for individuals who may be affected by AI predictions have created and developed an open-source Fairlearn package.

3. FairLens

A Python package that is available as open source is employed to clearly detect bias and assess data equity. In addition to providing several indicators of equality across a variety of legally recognized factors including age, ethnicity, and gender, the FairLens package can immediately reveal bias. Four points may be used to sum up the FairLens tool’s fundamental features:

• Measure the bias’s magnitude: Using statistical distances and measurements, the tool’s measures and tests enable the determination of the bias’s magnitude and significance.
• Identify Secured Properties: Measure the hidden links between legally protected qualities and other traits using the tool, which also gives methods for detecting these features.
• Tools for visual data representation: FairLens offers graphs of several variable kinds in subgroups of critical material, making it simple to spot and comprehend trends and patterns in data.
• Evaluation of Equity: Equity evaluation is a condensed method for evaluating the impartiality of an arbitrary dataset and for producing reports that point out biases and unreported correlations.

4. Aequitas

Aequitas is an adaptable open source toolbox that examines AI bias. To understand various types of biases and to make wise decisions regarding the creation and adoption of such systems, the application may be utilized to evaluate the forecasts of risk analysis instruments used in the fields of criminal justice, schooling, healthcare, employment services, and computer-based social services.

Two types of biases in the risk analysis mechanism may be found using the toolkit:

• Acts or treatments that are biased and do not adequately represent the population as a whole.
• Biased outputs are the outcome of a system mistake with respect to particular demographic groupings.

5. TCAV:

Test With Connection Active (TCAV), a technology and research project, was introduced by Google CEO Sundar Pichai at the 2019 Google I/O conference to find bias in machine learning models. Models may be examined by the system to find components that might be biased depending on factors like ethnicity, wealth, geography, etc. The primary way the TCAV system picks up “concepts” is through examples.

6. Google What-If Tool

What-If was developed by Google researchers and designers as a useful tool for those who produce machine learning algorithms. What equity do users want? is one of the tool’s most challenging and intricate issues concerning AI systems. With the help of this interactive, open-source application, users may explore machine learning models graphically. What-If tools, which are a component of the open-source TensorBoard tools, may evaluate data sets to show how machine learning models function under various circumstances and provide fruitful perspectives to explain model performance. The

What-If tool also enables users to alter data samples directly and analyze the effects of these changes using the corresponding machine learning model. Also, it is feasible to identify bias tendencies that were not previously discernible by examining the algorithmic fairness identified in the programme. What-user-friendly If’s graphical user interface makes it simpler for all users—not just programmers—to discover and verify models based on machine learning and find solutions to their challenges.

7. Skater

It is impossible to understand how variables are combined to produce predictions since the Black Box model is built based on the information and by algorithms, according to an Oracle project to de-blur it using a Python library. Skater contributes to the development of a practical machine learning system.

8. FairML

FairML is a framework for detecting bias in machine learning (ML) models. It does this by determining the relative value and significance of characteristics used in the ML model to find bias in both the linear and non-linear models. It can use characteristics like gender, race, religion, and others to uncover information that could be biased. It operates by measuring the relative relevance of the model’s input, which aids in determining the model’s fairness and auditing the prediction models.

9. Crowdsourcing

Crowdsourcing was utilized by Microsoft and the University of Maryland academics to precisely identify bias in applications for natural language processing.

The technique of enlisting the community to innovate, solve an issue, or improve efficiency is known as crowdsourcing. Crowdsourcing can be used to examine various aspects of the issue and find potential bias-causing factors.

The Implicit Association Test (IAT) served as the motivation for using crowdsourcing to identify bias in machine learning applications. IAT is frequently used by businesses and researchers to gauge and identify human bias. Crowdsourcing is primarily used to eliminate bias from data collection and cleaning, also known as data preparation, the first and most crucial stage in every machine learning application.

10. Local Interpretable Model Agnostic Explanations (LIME)

Wherever we look, machine learning applications are being employed. We are expected to fully trust the forecasts that these applications provide. These applications can be quite important at times, such as when utilizing machine learning to identify illnesses or in self-driving automobiles. Any inaccuracy in these forecasts might have disastrous consequences.

Before you can begin to address the issue if your model is producing inaccurate or flawed findings, it is crucial to comprehend why the model is producing these forecasts. Analyzing the model’s behavior can aid in identifying biases and eventually helping to reduce them.

LIME is a tool used to produce reasons for the behavior of various machine learning models. Lime gives you the ability to change the many aspects of your model so that you can better understand it and, if necessary, identify any bias.

Artificial intelligence (AI) systems have made it possible for individuals all around the world to test out new ideas and talents. They are now extensively utilized in a variety of contexts, from choosing books and TV shows to picking applicants for various positions to more delicate duties like illness prediction.

Large amounts of data must be collected and stored by AI systems in order to improve task accuracy. These data are then handled by smart system algorithms so that the system can learn from data patterns and characteristics and eventually develop the capacity of the machine to perform the tasks for which it was designed.

There is a greater demand for just systems with the ability to make or assist in the making of judgments that are devoid of partiality and prejudice as a result of the widespread use of AI applications in many industries and communities.

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Examples of AI-driven marketing biases in the recent past

Artificial intelligence (AI) has become an integral part of our lives, from recommending products to predicting medical outcomes. However, as AI becomes more widespread, there’s a growing concern about AI bias and its potential impact on decision-making.

AI bias refers to the systematic and unfair prejudice in the predictions or decisions made by AI models. This bias can occur in various forms, such as algorithmic bias, dataset bias, or cognitive bias. AI bias can lead to serious consequences, such as unfair treatment, discriminatory outcomes, or incorrect decisions.

1. Amazon Recruitment Tool:

One notable example of AI bias is the case of the Amazon recruitment tool, which was designed to automate the recruitment process by analyzing resumes and ranking candidates. However, the tool was found to be biased against female candidates, as it had learned from the male-dominated resumes in its training data. As a result, the tool consistently downgraded resumes containing women’s names or references to women’s colleges. Amazon had to abandon the project after realizing the extent of the bias and the implications it had on the recruitment process.

2. Facial recognition technology:

Another example of AI bias is facial recognition technology, which has been found to be less accurate in identifying people with darker skin tones. This bias is due to the lack of diversity in the training data, which has primarily consisted of lighter-skinned individuals. As a result, facial recognition technology can lead to incorrect identifications and potential racial profiling.

3. AI bias in Google’s online advertising system

The representation of women in CEO positions in the United States stands at 27 percent. However, a study conducted in 2015 revealed that only 11 percent of the individuals who appeared in a Google image search for “CEO” were women. An independent study conducted by Anupam Datta at Carnegie Mellon University in Pittsburgh a few months later revealed that Google’s online advertising system displayed high-paying jobs to men much more frequently than women.

Google responded to these findings by clarifying that advertisers can specify the individuals and websites to which their ads should be displayed, and gender is one of the criteria that companies can set. Although there have been suggestions that Google’s algorithm may have autonomously determined that men are more suitable for executive positions, Datta and his colleagues believe that this may have been based on user behavior. For instance, if only men see and click on ads for high-paying jobs, the algorithm will learn to display those ads only to men.

4. Apple – AI bias for judging creditworthiness based on gender

The AI bias with Apple’s credit card happened because the algorithm used to determine creditworthiness was trained on biased data. The algorithm considered a variety of factors, including an individual’s income, credit score, and payment history. However, it also took into account other data points that are not typically used in traditional credit scoring, such as how an individual uses their Apple device and how they shop.

It’s possible that the algorithm learned to associate certain behaviors with creditworthiness that were more common among men, such as making larger purchases or using Apple devices more frequently. This would explain why David Heinemeier Hansson and Steve Wozniak were both given significantly higher credit limits than their wives, despite having similar financial profiles.

Apple has since apologized for the incident and pledged to investigate and address the issue of bias in its credit scoring algorithm. They have also announced plans to launch a credit card for couples, which would allow both partners to share a credit limit and build credit together.

AI bias can also impact decision-making in various fields, such as healthcare, criminal justice, and finance. For instance, in healthcare, biased AI models can lead to misdiagnosis or incorrect treatment recommendations for certain populations. Similarly, in criminal justice, biased AI models can lead to unfair treatment of minorities or individuals from disadvantaged backgrounds.

To mitigate the impact of AI bias, it’s essential to identify and address the root causes of bias in AI models. This includes conducting a thorough analysis of the training data, using diverse and representative datasets, and implementing fairness metrics to monitor and detect bias.

Additionally, it’s crucial to ensure that AI models are explainable, meaning that the reasoning behind their predictions can be understood and audited.

5. AI bias example in the US healthcare system:

At a time when the nation is grappling with systemic prejudice, it is crucial for technology to help reduce health inequalities rather than exacerbate them. However, AI systems trained on non-representative healthcare data often perform poorly for underrepresented populations.

A 2019 study found that an algorithm used in US hospitals to predict which patients would require additional medical care favored white patients over black patients by a significant margin. The algorithm relied on patients’ past healthcare expenditures, which is an indicator of their healthcare needs and expenses. However, this approach led to biased results because healthcare expenditures are significantly related to race, with black patients spending less on healthcare than white patients with similar medical conditions.

To address this issue, researchers and a health services company, Optum, collaborated to reduce bias in the algorithm by 80%. However, if AI had not been scrutinized, it would have continued to discriminate against black individuals.

Final Words:

AI bias is a critical issue that must be addressed to ensure the fairness and reliability of AI systems. AI bias can lead to serious consequences, from discrimination to incorrect decisions, and it’s crucial to implement strategies that prevent, detect, and mitigate the impact of bias in AI models. By doing so, we can unlock the full potential of AI while ensuring that it serves the best interests of all individuals and communities.

We must understand that we cannot take AI algorithms o the basis of face value. The technology is new and there are new capabilities but there are a new set of concerns that one must be aware of. Understanding what is bringing the biases in the interpretation and how we can make use of tools and other techniques to reduce them is very crucial.