Bias in Artificial Intelligence
As more individuals and businesses see the benefits of Artificial Intelligence, interest in it is growing. However, there are also some valid concerns surrounding AI:
AI as a threat to humanity – singularity projected for the second half of the 21st century
Technological unemployment – 44% of workers projected to lose jobs by 2030
AI inheriting human biases via training – and thus rendering AI unreliable as such
This article will focus on the third point – biases, and provide insights, types, examples and perhaps some answers to all important questions around it.
Defining Bias
Bias in AI refers to the systematic and unfair discrepancies that occur in the outcomes produced by artificial intelligence systems. These biases stem from the data or assumptions that are used in the development of the algorithm. During the training process, if the data fed into the machine learning algorithm is skewed or unrepresentative of the broader population or scenarios, the AI will inadvertently learn these biases, leading to prejudiced or stereotyped outputs. Similarly, if the assumptions or decisions made by developers during the algorithm's creation are influenced by subjective or unfair biases, these too can be reflected in the AI's performance. The impact of AI bias can be significant, and it can lead to discriminatory practices or unequal treatment of individuals based on multiple factors. It's a challenge belonging to the ethical areas of concern and ethical use of AI technologies.
Types of Biases
COGNITIVE BIAS
Cognitive bias in AI manifests when the unconscious errors and heuristics that typically affect human judgment and decision-making are embedded into artificial intelligence systems. These biases originate from the human brain's attempts to simplify and efficiently process the vast information it encounters, which leads to systematic errors or skewed perceptions. There are over 180 different types of cognitive biases identified by psychologists, and the scope of potential influence is vast.
In the context of AI, these biases can infiltrate machine learning algorithms in two primary ways:
through the designers – designers might unknowingly introduce their own cognitive biases into the model during the development phase
through the training data – if the data used to train the AI includes these biases due to historical biases or is unrepresentative of the full scope of human diversity and complexity, the AI will likely learn and perpetuate these biases in its outputs
This integration of cognitive biases can significantly affect the fairness and accuracy of AI decisions, leading to outcomes that might favor one group over another or misinterpret information based on biased premises, therefore addressing this is crucial.
SAMPLE BIAS
The issue of lack of complete data is another significant source of bias in AI, known as sample bias. When the data used to train an AI system is incomplete or unrepresentative of the wider population or context it's meant to serve, the AI's decisions, predictions, and behaviors can become inherently biased. This type of bias occurs because the AI system's learning is confined to the scope and characteristics of the training data. For instance, if most psychological research studies — and consequently, the data derived from these studies — predominantly feature undergraduate students as subjects, the findings and any AI systems trained on this data might not accurately reflect or apply to other age groups, educational backgrounds, or demographics. This lack of diversity and completeness in the data can lead to AI systems that perform well for the specific group represented in the data but fail or act unfairly when dealing with individuals or scenarios outside of that dataset. Data should be completeness to ensure a high degree of representativeness. That is a rather difficult task requiring proactive efforts to include diverse and comprehensive datasets in AI training processes.
The Elusive Goal of Unbiased AI
While theoretically it is possible, achieving a complete unbiasedness in AI is realistically an unattainable goal. We have to keep in mind the origin of this – the human consciousness and intelligence, which is inherently biased almost always. Given the character of human biases and the fact that we still deal with major limitations in technology and methodology when it comes to this problem, it is unlikely to be defeated anytime soon.
Technically, if an AI's input data could be entirely cleansed of all conscious and unconscious assumptions related to ideological constructs, it could theoretically operate without bias, making purely data-driven decisions. But the practicality of this scenario is significantly challenged by several factors:
1. The data that feeds AI systems originates from the real world, which is inherently imbued with human biases. These biases are not only numerous but are also continuously evolving and being recognized, making the task of identifying and eliminating all biases from data a challenge due to expansive nature. Moreover, humans, with their own inherent biases, are responsible for creating, collecting, and curating this data, as well as designing and programming the AI algorithms themselves. This human involvement at every stage introduces multiple layers of potential bias.
2. The identification and removal of biases require an understanding and agreement on what constitutes bias, which can vary culturally and contextually. Even with continuous efforts into radically improving methodologies for detecting and mitigating bias, such as fairness metrics, bias audits, and diverse training datasets, the complete eradication of bias is improbable. Biases are numerous and can be very subtle, but they are also deeply embedded in the societal structures and historical data that AI learns from.
Our focus should therefore not be on erasure, but on minimizing and managing biases in AI. This involves continuous testing and reevaluation of both data and algorithms, employing responsible AI principles, and incorporating a diverse set of perspectives in the development process. It should include fostering explainability in AI systems to understand and mitigate biases when they occur. Best is to acknowledge the limitations and actively work towards ethical AI. It's possible to significantly reduce biases, though likely never completely eliminating them.
The Complex Quest OF BIAS MITIGATION
The first step in mitigating biases is to acknowledge their existence and origin, primarily rooted in human prejudices that can seep into datasets. Even with a complete dataset, the task of identifying and removing biases is daunting.
There have been proposals to “fix” the issue by using a rather simplistic approach – to remove protected characteristics from the datasets, or eliminating labels that directly contribute to biased outcomes. However, this method, known as fairness through awareness, is mostly ineffective and can lead to oversimplified models that lose critical contextual information, thus reducing the accuracy and effectiveness of the AI system. The removed attributes can still influence the model indirectly through other correlated features, a phenomenon known as proxy discrimination.
STEPS TOWARDS MITIGATION OF BIAS IN AI SYSTEMS
To mitigate unfairness and biases in AI, a deep understanding and continuous scrutiny of both the algorithm and the data are imperative. Initially, it is crucial to examine the training dataset to ensure it is sufficiently representative and expansive, and thus reducing the risks of common issues such as sampling bias. A dataset that accurately reflects the diversity and complexity of the real world helps in creating an AI system that is reliable across scenarios. Additionally, conducting subpopulation analysis is a great technique in this endeavor. By calculating and comparing model metrics for specific groups within the dataset, developers can identify disparities in the model's performance across different demographics, allowing for targeted improvements to ensure consistency. This process helps in pinpointing subtle biases that might not be evident in the general analysis.
Machine learning algorithms are dynamic in nature, and this necessitates their continuous monitoring over time. As these algorithms learn and evolve with new data or changes in their operating environment, their outputs might shift, potentially leading to new biases or exaggerating existing ones. Regularly tracking and evaluating the model's performance and fairness metrics over time allows for the early detection of such issues, and of course timely interventions. For the AI system to remain as unbiased and equitable as possible throughout its lifecycle, there needs to be an improvement in response to new data and insights.
The technical aspect of this strategy focuses on employing tools and methodologies that aid in the identification and understanding of potential sources of bias. These tools analyze the data and the algorithm's performance to reveal traits and patterns that might be influencing the accuracy and fairness of the model. They pinpoint these elements, and developers can then make informed adjustments to the data or the model.
On the operational front, the strategy is centered around improving the processes of data collection and model development. This includes the use of internal "red teams" specially designated to challenge and scrutinize the AI system to uncover hidden weaknesses. If possible, one can invite third-party auditors to provide an external perspective, ensuring that the system is thoroughly evaluated and vetted.
Organizational strategies focus on cultivating a workplace culture that prioritizes transparency and accountability in AI development. This involves presentation and regular reviews of metrics and processes related to AI systems.
Improving human-driven processes is a vital aspect as biases are identified within the training data. The process of building and evaluating AI models often brings to light long-standing biases that may have been unnoticed or unaddressed in the data or the decision-making processes it informs. To identify these biases is the first step; understanding and addressing the underlying reasons for these biases is where the real challenge and opportunity lie. Companies can use the insights gained from AI model development to get deeper into the reasons behind the biases, whether they stem from historical inequalities, data collection methods, or other sources. Once these biases and their roots are understood, the focus shifts to improving the actual processes through targeted training and process design, which might involve implementing more other data collection and analysis protocols with bias detection and mitigation steps throughout the AI development lifecycle.
Deciding on the appropriate use cases for automated decision-making versus those requiring human involvement is an important decision that should be guided by the complexity, sensitivity, and potential impact of the decisions being made. Automated decision-making is best suited for scenarios where tasks are routine, well-defined, and require consistency and speed, for example data entry, scheduling, and certain types of customer inquiries. Automation in these cases increases efficiency and reduces human error, leading to more reliable outcomes. Human involvement becomes crucial in scenarios that involve complex decision-making, require understanding of context, or have significant ethical, legal, or personal implications. Decisions related to healthcare, legal judgments, or personal welfare fall into this category.
The decision should also consider the potential risks and biases inherent in the AI system. In high-stakes scenarios or where there is a high risk of bias, human oversight is necessary to ensure fairness and accountability. Additionally, in areas where public trust is essential or where the consequences of errors are severe, involving humans provides a layer of scrutiny. Ultimately, the goal is to leverage the strengths of both AI and human judgment in a complementary manner.
takeaway and conclusion
The final takeaway from the extensive discourse on AI biases is that while complete eradication of bias is an ideal yet unattainable goal, significant progress can be made through persistent strategies. From a technical perspective, the deployment of tools to detect, understand, and mitigate biases is crucial, covering comprehensive and representative datasets, performing subpopulation analysis, and monitoring and updating the models as they evolve. Operationally, the key is to improve the data collection methods by auditing.
Recognizing the importance of human oversight in AI decision-making processes acknowledges the limitations of technology and the important role of human judgment, empathy, and consideration of nuances. Decisions on when and how to integrate human oversight should be guided by the nature of the task, the potential impact of decisions, and the risks and biases of the AI system. Acknowledging the limitations is paramount, paired with leveraging the strengths of both AI and human capabilities will move us closer to a grounded and responsible AI.