A Literature Review: Bias Detection and Mitigation in Criminal Justice ^†

Abstract

The use of algorithmic models or systems in criminal justice is increasing day by day, yet the bias in these algorithms can perpetuate historical inequities, especially in predictive tools like COMPAS. This literature survey examines 30 studies addressing algorithmic bias in criminal justice. Key topics include bias types, bias detection metrics or variables such as demographic parity and equalized odds, and bias mitigation techniques like re-weighting and adversarial debiasing. Challenges in achieving fair and unbiased predictions are highlighted, including ethical considerations and trade-offs or a balance between fairness and accuracy. Insights from COMPAS and similar systems underscore the need for ongoing research, proposing potential directions for policy and practice.

1. Introduction

Algorithms are being widely used in high-stake applications like criminal justice these days. These algorithms are being used for tasks like risk assessment, parole decisions, and sentencing people [1]. Algorithmic bias has emerged as a complex and critical challenge in machine learning models. The algorithms are normally and often seen as an objective tool that can improve efficiency and minimize the errors made by humans, but some studies show that these algorithmic systems or models, like Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), can unintentionally learn and reinforce unfair patterns or stereotypes from the data it was trained on, causing it to make biased decisions or sentencing in this criminal justice context that shows these societal biases [2]. Such biases usually come from the patterns or information in historical data that have important ethical and social effects [3].

In criminal justice systems, they can lead to unfair results for underrepresenting groups, which can worsen issues in areas like bail, parole, and sentencing. Therefore, the absence of bias in these algorithms is important in making the decisions fair for everyone [4]. As already mentioned, one of the well-known algorithms used in the criminal and felonious justice system is the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), which is a tool for risk or threat assessment that evaluates an individual’s or existent’s likelihood of recidivism, which means the chance or likelihood that they will commit another crime after being released, aiming to make judicial decisions or opinions about bail, parole, and sentencing [5]. However, research has shown that COMPAS may unfairly label people differently based on race which raises questions about how fair and accurate the algorithm is [6]. One of the studies by ProPublica found that COMPAS often rates black defendants as more likely to commit a crime after being released, while rating white defendants as less likely. This has triggered many strong debates about how it affects justice [7].

The purpose of this literature review is to bring together current research on ways to detect and mitigate bias in criminal justice algorithms. Although this review focuses on COMPAS as a well-studied example, we will also look at methods that apply to similar algorithms. This review aims to highlight the pros and cons of different bias detection metrics, like comparing the outcomes across groups (demographic parity) and ensuring fair chances (equalized odds) [8], as well as bias mitigating methods like data re-weighting and algorithmic adjustments to see how these methods can be used practically and evaluated fairly [9].

2. Algorithmic Bias in Criminal Justice

As machine learning algorithms gradually make decisions on matters of parole, sentencing, and risk assessment in criminal justice, there are growing concerns about the prevalence of algorithmic bias. Algorithmic bias describes systematic and unfair discrimination against certain groups due to errors in the data, model design, or deployment. In the criminal justice system, therefore, biased algorithms enhance existing disparities in outcomes and result in disproportionate impacts on a long list of marginalized groups. These biases are deeply embedded in historical data, providing examples of past prejudice or system inequities, and if those data are used as training data for predictive models, the algorithms learn and enforce those patterns [10,11].

Bias in criminal justice can be understood to take the form of racial bias, gender bias, and socio-economic bias. Racial bias has emerged as one of the top areas of research in which most of the studies have been conducted. This is because of the historical occurrence of racial disparity or inequality in the criminal justice system. Algorithms such as COMPAS might possibly misestimate the likely rate of recidivism between races. For instance, the popular report from ProPublica showed that COMPAS was more likely to wrongfully brand black defendants as at high risk to reoffend compared to white defendants who had similar backgrounds, and the white defendants were wrongly labeled based on historical data as being low-risk [12]. Such bias includes individuals but also reinforces stereotypes and has led to harsh legal penalties for some groups, further solidifying social imbalances [13].

This makes COMPAS an important case study when tracing the consequences of algorithmic bias in criminal justice: originally designed to provide data-driven support to judicial decisions, a risk score is generated on the basis of factors such as age, criminal history, and behavioral traits. It has widely been criticized, however, for a lack of transparency in that its algorithm is proprietary and therefore not easily auditable by external auditors on the decision-making process [14]. The lack of transparency has resulted in limited opportunity for scrutiny and discussions on the possibility of bias and calls for more transparent and interpretable algorithms, especially in sensitive areas such as criminal justice [15].

The most popular bias detection metrics used in judging or measuring fairness of criminal justice algorithms are known as demographic parity, equalized odds, and disparate impact. First, demographic parity enables all demographic groups to yield equivalent outcomes, which can be useful in tracking unfair biases in the model, although not particularly by predictive accuracy [16]. Another measurement is equalized odds, comparing true positive (TP) and false positive (FP) rates across groups in order to detect if there are significant differences in outcomes across demographic categories [17]. One form of disparate impact identifies whether a protected group is adversely affected at a rate disproportionately higher than other groups; it can uncover biases that might otherwise be buried [18].

Bias detection and correction are not easy. An important tension in criminal justice is this balance between fairness and predictive accuracy: often, the underlying data used to train such model algorithms are inherently biased as they reflect historical patterns of discrimination. Examples would include such widely applied data as arrest records in risk assessment models, which are inflected with socioeconomic and racial biases in policing. But using these data, the algorithm, though accurate in its predictions, would unwittingly perpetuate all such biases in these data [19]. Here is one clear trade-off involved in bias mitigation: fairness often comes at the cost of a certain level of predictive performance, which means the utility of the algorithm in practice could be compromised [1].

Another major concern is that in criminal justice algorithms, the sensitive variables, such as race, gender, or even socioeconomic status, tend to be treated as protected attributes and are never used directly in the model. However, indirect proxies or surrogates of these attributes persist throughout the data and introduce an additional unintended biasing effect. This would actually demand very advanced techniques to identify and rectify the proxy variables; the added complexity would raise technical as well as ethical dilemmas [2]. For instance, data re-weighting and adversarial debiasing comprise two approaches to handling bias [3]: either by changing the weight of biased points or detection of biased patterns and its removal through the use of an adversarial network [20,21].

In sum, issues both ethical and practical are real in the pervasiveness of bias in criminal justice algorithms. Although COMPAS and similar systems are meant to help with fair, informed judicial decision-making, they can, if not caught and corrected, propel disparities forward. Thus, it is the challenge of all concerned—researchers, policymakers, and practitioners alike—to decide how best to address such biases so that algorithmic decision-making can contribute to rather than detract from the pursuit of justice.

3. Methodology

It is very important for bias in criminal justice algorithms to be detected, especially in cases where unfair or disproportionate outcomes appear against certain groups of people. This section discusses the various techniques and metrics that have been applied for measuring bias, always in the context of their use for criminal justice systems like COMPAS, and the challenges for those methods.

3.1. Overview of Detection Techniques

Algorithmic bias is detected by trying to define what “fair” outcomes are. Bias in criminal justice detection relies on disparate treatment of predictive accuracy and outcomes across different demographic groups. The most commonly used metrics for fairness are demographic parity, equalized odds, and disparate impact, all of which describe different views of fairness.

3.1.1. Demographic Parity

This is also known as statistical parity. It compares whether the chance or probability of obtaining a positive result is the same for all groups, irrespective of the accuracy of their predictions. For example, in an algorithm about crime prevention under the rubric of the criminal justice system, demographic parity entails that individuals from different races ought to have the same chances of being classified as either low-risk or high-risk, even for whatever real risk of recidivism they have [5]. Although demographic parity is intuitively understandable, it does not take individual risk factors into account and, therefore, is not very informative in a setting where inherent risk levels are different across groups [6]. Demographic equality ensures that the probability of a positive prediction or the vaccination is the same for all the groups:

$$P(X=1|A=a_1)=P(X=1|A=a_2)$$

where

• X is the predicted outcome (e.g., high-risk or low-risk).
• A is the sensitive attribute (e.g., race, gender).
• The a₁ and a₂ are different values of A.

3.1.2. Equalized Odds

Equalized criminal odds checks whether an algorithm generates equivalent true positive and false positive rates for any group. For instance, in a criminal justice application, equalized odds will demand that the COMPAS instrument has equal counts of true positives (TPs) and false positives (FPs) between groups such as black people and white people. The advantage of this measure is that it focuses on the prediction itself but is more concerned with overall accuracy, hence seeking balance in outcomes. However, equalized odds may be difficult to realize in practice, but not necessarily in theory, especially in real-time applications where accuracy and fairness metrics might come into conflict [7,8]. Equalized odds need equal true positive rates (TPRs) and false positive rates (FPRs) across groups.

$$P(X=1|Y=1,A=a_1)=P(X=1|Y=1,A=a_2)$$

$$P(X=1|Y=0,A=a_1)=P(X=1|Y=0,A=a_2)$$

where

• Y is the actual outcome (e.g., recidivism or non-recidivism).

3.1.3. Disparate Impact

This concerns whether a protected group, say on the basis of race or gender, suffers an outsized adverse impact from an algorithmic decision, as opposed to other groups. This metric is of special significance in the legal and regulatory context, as it will measure whether certain groups experience worse outcomes at a significantly higher rate than others. Disparate impact is useful in criminal justice as it can pick up on instances where an algorithm disadvantages a particular group indirectly, even if other fairness metrics appear satisfied [9]. However, it falls short in sensitively distinguishing between lawful variations and biased practices [10]. Disparate impact measures the ratio or rate of positive issues or outcomes between the groups:

Disparate impact $= P(X=1|A=a_1)/P(X=1|A=a_2)$. The value far from 1 indicates “bias.”

3.2. Application to Criminal Justice Algorithms

Bias detection measures such as demographic parity, equalized odds, and disparate impact have begun to be applied to the assessment of criminal justice algorithms like COMPAS. For example, several studies have applied these metrics to observe the classification that COMPAS makes based on a person’s race. During disparate impact analysis, an investigation by ProPublica revealed huge disparities in risk predictions made through COMPAS on racial grounds that indicated while risk predictions were supposed to match recidivism, black defendants were frequently labeled or classified as high-risk even when controlled for actual recidivism rates [11]. This calls for consistent detection protocols of bias and begins ethical discussions on such disparities.

Yet another project relies on equalized odds to test whether TP and FP rates of the COMPAS algorithm balanced the case across racial groups. In this case, it was discovered that “evened or equalized odds were not achieved in that Black defendants faced a high rate of false positives, while White defendants witnessed a higher false negative rate.” This imparity in prediction also highlights the limitation of employing predictive accuracy as a measure of fairness and instead prefers the application of bias detection methods that measure outcome for more than one demographic group [12].

3.3. Challenges in Bias Detection

Although there are various metrics for bias detection, the application of these methods poses several challenges. To start with, a lot of data used in training criminal justice algorithms often carry historical biases. Training model algorithms on biased data may perpetuate them and thus reflect similar patterns, making it challenging to isolate the risk’s real reflected patterns from those due to systemic inequalities [13]. This includes criminal records, which are considered mainly in risk assessment tools although faced with socioeconomic and racial biases in the law enforcement practice, thus leading to biased predictions in algorithmic systems [14].

The other challenge involves the trade-off between accuracy and fairness. In criminal justice, high predictive accuracy is necessary for ensuring risk assessment tools can dependably find out who needs to be targeted for preventing reoffending. However, improvements in metrics of fairness, such as demographic parity and equalized odds, decrease the predictive capability of an algorithm. This trade-off raises tricky questions of ethics because the push toward fairness can be at the cost of accuracy and, thereby, erodes trust in the public for the reliability of the algorithm [15]. Hybrid approaches have been proposed that seek a balance between accuracy and fairness, but achieving a solution proves challenging, particularly in such high-stakes domains as criminal justice.

Additionally, most of the fairness metrics are incompatible directly with each other, so they cannot be applied in unison. For example, it is often simply not possible to achieve both demographic parity and equalized odds unless the rates of particular outcomes change profoundly between groups. This can be argued as to why sensitivity toward context-specific goals of the algorithm and the legal standards surrounding fairness is needed by discussing pros and cons of demographic parity, equalized odds and disparate impacts metrics for bias detection as shown in

Table 1. Pros and cons of bias detection methods.

Metric	Pros	Cons
Demographic Parity	Simple to understand. Highlights group-level disparities.	May overlook individual-level fairness. It does not account for differences in base rates.
Equalized Odds	Balances fairness and accuracy. Directly compares the prediction outcomes.	Difficult to achieve when base rates differ significantly. Computationally intensive.
Disparate Impact	Relevant in regulatory contexts. Identifies indirect bias.	Cannot always differentiate between legal and illegal disparities. Requires interpretation.

[16,22].

3.4. Recent Advances in Detection Techniques

Recent advances have attempted to mitigate some of the same limiting factors by introducing even more sophisticated detection techniques that go beyond traditional fairness metrics. For example, methods in adversarial debiasing train models to minimize bias by penalizing disparities in outcomes during training. This approach has shown promise towards achieving the general purpose of reducing bias in high-stakes applications, though proper implementation will determine avoiding over-correction at the cost of predictiveness [23]. Other research also proposes using counterfactual analysis to generate hypothetical scenarios under which the analyst can test whether, conditional on characteristics independent of race and gender, an individual’s risk score would shift. Such a technique would better serve to illustrate how algorithms work and could potentially bring greater fairness in criminal justice algorithms [20,24].

Generally speaking, the task of detecting bias in criminal justice algorithms is difficult and open-ended, with multiple challenges and limitations. Metrics and techniques discussed here illustrate the intricate nature of the pursuit of fairness in outcomes because that which achieves one point of equity tends to compromise the other. AI-based decision-making systems or tools have really shown significant potential in streamlining legal processes, which reduces the human bias and improves the overall judicial efficiency [25]. So more and better methodologies need to be developed, which can better address these trade-offs and help with both ethical AI practices and equitable outcomes in criminal justice applications.

4. Bias Mitigation Techniques

Bias mitigation techniques are appropriate response strategies to deal with ethical considerations resulting from algorithmic bias in criminal justice systems. Therefore, the three approaches may include methods of pre-processing, in-processing, and post-processing techniques.

Pre-processing Techniques: These techniques preprocess the training data before passing it to an algorithm. This aims to remove all the biases from the dataset. The popular methods include those in which more importance is given to the instances of underrepresented groups so that the influence of various demographic groups on learning happens in a balanced way. Friedler et al. demonstrate that re-weighting could enhance the fairness level within risk assessment algorithms where the contribution of various demographic groups would be made equal [17]. However, a major problem with pre-processing-based approaches is that there could be the loss of useful information from the original dataset, which leads to reduced predictive performance [19]. Re-weighting assigns weights to training samples to balance their influence on the model:

$$w_i=P\left(A\right)/P\left(A\right|X_i)$$

where

• $w_i$: Weight assigned to the ith data sample.
• $P\left(A\right)$: Probability of the sensitive attribute A.
• $P\left(A\right|X_i)$: Conditional probability of A given feature Xi.

In-processing techniques: These adjust the algorithm at train time. Specifically, one can simply modify the learning algorithm to enforce fairness constraints. A recent trend is called adversarial debiasing. This has included the development of a model that is trained to make predictions simultaneously while minimizing the predictability of demographic group membership from its outputs. The aim here is for fairness without compromise on accuracy [1]. However, these may be computationally intensive and require sophisticated algorithm designs, which might limit the applications of these methods to a very few practitioners in research [2].

$$min\left[L\right(X,Y; \theta )-\lambda L_A(A,B; {\theta }_A\left)\right]$$

where

• $L(X,Y; \theta )$: Loss of the main prediction task (e.g., classification).
• $L_A(A,B; {\theta }_A)$: Adversary loss for predicting A from B.
• $\lambda$: Trade-off parameter between prediction accuracy and fairness.

Post-processing techniques: These techniques fine-tune the outputs of an already trained model so that they are fair. Methods used here include threshold adjustments. Here, cut-off values are set for various demographic groups in such a way that they attain equalized odds. For example, in COMPAS, some threshold adjustment may potentially alleviate racial disparities in recidivism risk determinations [3]. But this also creates ethical dilemmas as it could compromise total accuracy to equity, leading to undesirable outcomes in particular conditions [4]. Post-processing adjusts thresholds to achieve equalized odds or demographic parity across groups. Adjust decision thresholds for each group: $A=a_1, A=a_2$ such that $T_{a1}$ is not equal to $T_{a2}$, where $T_a$. The thresholds can be tuned to ensure $P\left(X=1|A=a_1,Y=y\right)=P\left(X=1|A=a_2,Y=y\right).$ This enforces equalized odds across groups. The advantages and disadvantages for bias mitigation techniques namely pre-processing, in-processing and post-processing techniques has been discussed in the following

Table 2. Pros and cons of bias mitigation techniques.

Technique	Example	Pros	Cons
Pre-Processing	Re-weighting, data augmentation.	Simple to implement works with any model.	Risk of losing important data characteristics.
In-Processing	Adversarial debiasing, fairness-aware learning.	Directly integrates fairness into the model.	Computation is expensive and complex to design.
Post-Processing	Threshold adjustments, calibration.	Easy to implement, works with pre-trained models.	May reduce accuracy in certain applications.

.

Besides these, ethical considerations play a prominent role in applying the bias mitigation techniques. The practitioners should observe the trade-off between fairness and accuracy since many of the mitigation strategies have a negative impact on performance degradation [5]. Furthermore, the design and decision-making processes of the algorithm should be transparent for the stakeholders and the affected communities to gain trust. In addition, ethical frameworks must be set in place while practicing the appropriate mitigation strategies that fit into the principles of justice and fairness as well [6]. Bias mitigation frequently involves trade-offs between fairness, accuracy, and ease of implementation or perpetuation.

Table 3. Trade-offs in bias mitigation.

Metric	Pre-Processing	In-Processing	Post-Processing
Fairness	Moderate	High	High
Accuracy	High	Moderate	Low to moderate
Ease of Implementation	High	Moderate	High
Scalability	High	Moderate	Moderate

summarizes these trade-offs across pre-processing, in-processing and post-processing techniques.

5. Fairness in AI and Criminal Justice

Fairness in algorithmic systems is difficult to define and context-dependent [12]. In criminal justice, ‘fairness’ usually means to minimize ‘discrimination’ against disadvantaged groups, but at the same time, in their predictions, there should be accuracy [13,14]. And dealing with the trade-off with fairness and accuracy, the findings of most of the studies reveal that achieving both at the same time often proves to be problematic [15]. Recent studies have also highlighted the growing dependencies and reliance on AI predictive policing models that are used to analyze crime patterns and improve law enforcement decision-making [23] For example, predictive policing algorithms may unintentionally ‘lock-in’ biases in historical data while predicting crime, thus overpolicing through policies or actions that have more instances of police or enforcement presence over specific neighborhoods or groups [16,17]. It is from such literature that the importance of defining fairness within an application boundary and the stakeholder’s role to apply fairness metrics is witnessed [18,19].

However, more research on the application of algorithms in the criminal justice system should uphold openness and accountability within the design of such tools and their impact. It has also amplified the significance of bringing in various voices, experts, communities, and affected groups in making approaches toward designing fairness measures. This way, the quality of fairness metrics is going to reflect practical values and concerns, making the system even more accountable and trust-gaining [1]. Transparency and accountability work in ensuring such tools are applied equitably and justly rather than perpetuating biases or disproportionately affecting certain groups [2]. This will likely make the public conscious about the workings of such systems and the risks associated with their application.

6. Insights from COMPAS and Other Algorithms

The COMPAS algorithm (Correctional Offender Management Profiling for Alternative Sanctions) is an iconic example in studies on algorithmic bias in the criminal justice system [3]. While algorithms or computational methods do give enhanced predictive capabilities or opportunities, there will always be concerns regarding fairness, bias, and responsibility (accountability) [26]. Studies revealed stark racial disparities in outputs coming from COMPAS, particularly in its high false-positive rates for black defendants compared with white defendants [4]. Such disparities, therefore, raise serious ethical concerns as regards reliability and fairness in the assessment of risk when deciding judicial outcomes; hence, there must be scrutiny of predictive policing tools [27,28,29].

Comparison of COMPAS with other comparable algorithms that are used in decisions related to pretrial detention and parole reveals that the problems of COMPAS are not unique; other algorithms used similarly bias their outputs [5]. Algorithms similar to COMPAS tend to contain systemic biases simply because they were trained on historical data, which may reflect and sometimes even serve to perpetuate existing racial and socioeconomic inequalities [6]. These biases are exacerbated by the obscurity of algorithm design, where details of the underlying mechanisms and sources of data are often proprietary and thus impossible to assess externally and provide little recourse for those harmed by the algorithm’s recommendations [7,8].

Through cross-case analysis, it can be seen that tools like COMPAS and similar algorithms require strong validation frameworks that assess not only statistical accuracy but also fairness and societal impact. The construction of these algorithms often raises concerns of transparency [30], and the reliance on biased data often leads to disproportionate results [9]. Some would argue for “fairness constraints” in algorithm design, which might minimize but not eliminate inequalities. Fairness constraints often have a downside, however: striving for strict equity among groups may compromise predictive accuracy in the population at large [10].

In addition, taking into account other algorithmic programs developed in the criminal justice system, it is obvious that there is an acute need for ethical rules and regulatory checks. Machine learning models like neural networks and decision trees have also been applied in the criminal data analytics field efficiently and effectively to enhance crime prediction and detection rates [24]. The use of fair-aware machine learning algorithms that respect the protected attributes so as not to exacerbate more discrimination has been suggested. Such solutions are tough since fairness conflicts with the accuracy of the model and societal costs simultaneously. The insights from studies on COMPAS and similar algorithms reinforce that while algorithmic tools offer efficiency, they must be rigorously evaluated to avoid perpetuating harm. Comprehensive assessments, including both quantitative and qualitative reviews, are essential to address biases and foster algorithms that serve justice equitably and ethically.

Table 4. Key insights into the COMPAS algorithm.

Aspect	Details
Purpose	It predicts the recidivism (likelihood of reoffending) to obtain or aid in bail, parole, and sentencing decisions.
Input Features	Age, criminal history, prior arrests, employment status, education, drug history, and more.
Scoring Mechanism	Likely based on a weighted sum or logistic regression to produce a risk categorized as low, medium, or high risk.
Strengths	Provides data-driven decision support and improves consistency in judicial decisions.
Critiques	Black defendants were disproportionately labeled as high-risk; the algorithm lacks transparency and indirect bias due to historical data patterns.
Alternative	Transparent algorithms that use fairness constraints, adversarial debiasing models, and open-source tools for recidivism prediction.

summarizes the core aspects of COMPAS, including its inputs, functionality, and key criticisms.

7. Future Directions in Bias Detection and Mitigation

Despite the improvements in algorithmic fairness, there is still much work to be carried out to bridge research gaps in detecting and mitigating bias [11]. Future efforts should target the development of responsive frameworks for checking the fairness of any application context, particularly in high-stakes areas, such as criminal justice, where biased algorithms may have serious effects on the outcome [12].

Some emerging methods fairness-aware machine learning models and interpretability techniques—show promise in enhancing transparency and accountability in AI systems [13]. By design, fairness-aware algorithms directly counterbalance potential biases, whereas interpretability methods can help stakeholders understand the decision-making process, thus making it easier to identify and correct biases at the onset [14]. Most challenging, though, is to implement such methods on account of complex trade-offs between fairness and accuracy, which require careful calibration.

The charge for effective interdisciplinary collaboration to promote positive, meaningful policy change and practice transformations comes down to policymakers working closely with technologists, ethicists, and affected communities to craft regulations that are fair but do not compromise public safety requirements [15]. Ethical and social considerations should be integrated into the fabric of algorithm development, testing, and deployment processes [16]. Standards and benchmarks that can comparatively evaluate the performance of bias mitigation techniques in different contexts are needed. A joint, transparent, and ethically informed framework can improve algorithmic fairness and facilitate more equitable systems for all communities.

8. Conclusions

In conclusion, putting it all together, the issue of anti-biasing algorithms, especially criminal justice algorithms, represents one of the biggest challenges to fairness and equity issues. This paper discussed several techniques for bias detection and reduction, reviewed important definitions of fairness, and discussed in some detail the popular COMPAS tool, with a focus on several racial biases of the tool. More research should be performed, but for now, perhaps new technologies might require challenges that require updated Standards and Guidelines of Fairness.

Algorithms will carry with them consequences that are more than just a matter of numbers or technical bugs; they will involve real people and take in for themselves broader ethical and social questions. Bias in the algorithm could have implications in public trust concerning criminal justice, deepen social inequalities, and sometimes produce skewed results, such as in certain aspects relating to bail and parole and at sentencing. For such reasons alone, therefore, there is an imperative to ensure fairness, transparency, and accountability in any technology brought into justice systems.

Better cooperation among AI researchers, policymakers, and the communities targeted by these tools is a call for building a fairer future. If these groups work together, they can guide the policies and standards that will ensure AI is applied ethically, whereby everyone is treated fairly without prejudice and discrimination. With justice over pure efficiency priorities, we can be assured that AI really does work for everyone’s advantage.

Ethical AI shall support fair and just decision-making, as well as continuous efforts in research, policy, and transparency. It is only in this way that technology can be a true instrument of justice, driving forward societal development and not reinforcing old inequalities. It will build systems that people can trust.