As applications of artificial intelligence AI have entered multiple spheres of human life during the recent years, this innovative concept stimulated a wide range of concerns and questions about its advantages and disadvantages, and potential positive and negative impacts on a society. A growing interest today is the deployment of AI in social justice. The opportunities are large for AI to resolve for social injustices, increase voice to justice and being fair. However, with all those opportunities come essential ethical dilemmas that should be solved. This blogpost discusses the impact of AI for social justice and the various challenges that are linked to its use.
Understanding AI’s Role in Social Justice – The Role of AI in Social Justice
It is necessary to take a brief look at what social justice implies before having a look at the prospects and threats that AI brings to this field. It means ideas of equity, equality of race, colour, gender, income and status in the distribution of resources and opportunities. In this case, AI has the ability of making societies better through augmenting systems that are relevant in the provision of education, healthcare, criminal justice and employment.
In what ways will the use of AI benefit social justice?
If harnessed properly AI can address several social justice causes. Below are some of the ways AI is being used to promote fairness and equality:
1. Use of Decision-Making Systems in Reducing Preconceived Notions
AI also comes with the flexibility of the ability to detect biases in decision making. For example, in the criminal justice, AI can be utilized to study previous court sessions, and show where discriminant might have been made by the court in relation to race, gender, or social class, when issuing out a particular sentence or granting paroles. Compas which stands for Correctional Offender Management Profiling for Alternative Sanctions has been apart of the process of evaluating the risk of recidivism as well as apart of the decision making process in regards to sentencing. These tools when applied ought for purpose of reducing human bias aid the processes of the judiciary to be fair.
2. Opening up the Opportunity for Education and Health Care
The five areas of utilization of AI include; While dealing with the issue of inequality, AI can enhance the availability of education and healthcare services to the neglected groups of society by delivering industry-specific services. Appreciatively, AI offerings can also personalize learning resources for students with disability or coming from disadvantaged backgrounds. In healthcare for example, telemedicine is an AI technology which avails consultation services hence very useful in areas where they are few or difficult to access.
3. Efforts in Expanding the Status of Employment
There is potential to use AI algorithms in order to find the right match between applicants and vacancies, especially if it is a minority or a discriminated group. For instance, while using the AI-based platforms to sort through applicants can reduce or even eradicate prejudice, it will not consider color, gender, etc., rather, it will consider the qualifications. Also, AI can make the work environment for disabled individuals better for various people with disabilities through assistive technology.
4. This paper focuses on two main aspects namely; Predictive Policing and Criminal Justice Reform.
Based on historical data, crime rates could be forecasted reducing the chances of crimes taking place as with predictive policing, an AI system. But, if not well controlled, it may enhance biases and bring higher police enforcement to some specific areas. However, one has to understand that the machine learning models when trained right can be fine- tuned not to give a Discriminating Bias in their results and therefore can help achieve a more fair policing.
How AI Can Promote Social Justice
AI has the power to tackle numerous social justice issues. Below are some of the ways AI is being used to promote fairness and equality:
1. Bias Mitigation in Decision-Making
AI has the ability to help identify and mitigate biases in decision-making processes. For instance, in the criminal justice system, AI can be used to analyze past legal cases and highlight potential biases in sentencing or parole decisions based on race, gender, or socioeconomic status. Tools like Compas (Correctional Offender Management Profiling for Alternative Sanctions) have been used to assess the risk of reoffending and to recommend sentencing. When used appropriately, these tools can help reduce human bias and make judicial processes more equitable.
2. Enhancing Access to Education and Healthcare
AI can improve access to education and healthcare for marginalized communities by providing tailored services. AI-powered platforms can adapt learning materials to meet the needs of students with disabilities or those from disadvantaged backgrounds. In healthcare, AI technologies such as telemedicine can offer remote consultations, which are particularly valuable in rural or underserved areas.
3. Increasing Accessibility in Employment
AI tools can aid in matching job seekers to opportunities, especially those from historically underrepresented groups. For example, AI-based platforms can help eliminate bias in hiring processes by focusing solely on a candidate’s qualifications, rather than making decisions based on gender or ethnicity. Additionally, AI can help create better work environments for people with disabilities by enabling assistive technologies that make the workplace more accessible.
4. Predictive Policing and Criminal Justice Reform
Predictive policing, which uses AI to predict where crimes are likely to occur, has the potential to reduce crime rates. However, if not carefully monitored, it can reinforce existing biases and over-police certain communities. Despite this risk, machine learning models can be fine-tuned to prioritize fairness and avoid reinforcing discriminatory practices, thus contributing to more equitable law enforcement.
The Positive Impact of AI in Social Justice Initiatives
Several initiatives and organizations are already using AI to make strides in promoting social justice. For example:
- The AI for Good Global Summit, organized by the United Nations, focuses on leveraging AI for social good, with specific applications in areas like disaster response, gender equality, and reducing poverty.
- Data Science for Social Good is an initiative that works with government agencies and non-profits to apply data science, including AI, to societal issues such as homelessness, healthcare, and criminal justice reform.
- AI for Human Rights, a platform developed by organizations like Amnesty International, uses AI to identify human rights violations by analyzing large datasets of media reports, social media, and other online sources.
They gather concrete examples from the presented use cases and show how AI is making social contributions and resolving problems that were previously ignored. Through promoting better prerequisites to necessary services, better frameworks on decision-making processes, and better eliminating systems of social injustices, AI represents an essential part for social justice.
Ethical Challenges of AI in Social Justice
The undeniable benefits of AI for driving positive changes are that social justice can also benefit from this technology. These are related to issues of bias, flexibility, trust, autonomy privacy and the possible reinforcement of existing disparities. These are some of the ethical issues, that must be well understood to avoid misuse or inequality in the use of Artificial Intelligence in the fight for Social Justice.
1. Bias in AI Algorithms
That is why, one of the most vital ethical questions related to the application of AI in social justice is algorithmic bias. These AI solutions are learned from databases that contain data with historical disparities in bias, prejudiced, and unfair treatment. If these datasets contain bias information, then the AIO learning system may pass these biases within the decision-making system.
For example, current AI systems used for identifying candidates for a certain position, or for criminals, may contain even all discriminations that have ever existed in hiring practices, or policing, etc. Research has found out that software used in hiring discriminates in a way showing preference to male candidates over female candidates or white candidates over any other color. Likewise, the use of predictive policing can lead to an unjustified targeting of groups who have reportedly been the victims of prejudice in the lineage of procedural racism.
Example: COMPAS Algorithm
Another most famous recent example of racial bias in AI is COMPAS algorithm employed in the criminal justice system of the United States. Numerous analyses have revealed that COMPAS was more likely to predict a high risk of recidivism for Black defendants rather than similarly situated White defendants. Such decision may no longer be fair and this cuts across the essence of social justice that is all About fairness.
If problems like this occur, the necessary solution is to make sure that the AI systems are trained with datasets which are diverse and inclusive. Moreover, AI models must be audited constantly, and the models have to be updated not to exacerbate existing disparities.
2. Corporate governance issue such as lack of transparency and accountability
There is little regulation regarding the decision that is made by AI hence the following is the next ethical concern. Most of the AI systems in existence today, especially those running on deep learning models, are inconspicuous; thus, it is very hard to fathom why a particular result is arrived at. This lack of transparency can be problematic and in particular in environment where decisions are likely to have social impact like criminal sentencing or job offerings.
Looking at social justice, the reason is that opacity leads to a lack of trust in AI systems among individuals. If generic populations are becoming of the opinion that Entry Structures are deciding on issues that affect them yet no accountability or justification is given, then the probability of ACCEPTANCE diminishes. However, when AI systems are used to take biased or unfair decisions, no one can really be said to be responsible for it because no one can clearly explain how the decision was arrived at.
Quote:
Transparency then has been established as a very important virtue of ethical AI. This could be so aptly summarized in the words, where one the one hand, we have the quote, ‘Without transparency, there is no accountability, and without accountability, there is no justice.’ — Dr. Timnit Gebru self employed AI researcher advocating for Ethical AI.
To address this problem, research is underway to design and build xAI techniques that would increase the interpretability of the AI systems. XAI attempts to explain how AI models reach specific conclusions so that the machines can be validated to be reliable and responsible.
3. Fear for the privacy and surveillance
The use of AI in social justice frameworks also come with issues to do with privacy especially where the AI is used in the surveillance of persons or groups. Promising technologies such as facial recognition, and location tracking may pose a threat to citizens’ privacy rights, in a way that is especially vulnerable to minority communities.
For instance, the AI-enabled policing initiatives in use to monitor criminal activities may promote racism thus violating human rights of the black population through mass surveillance through-the-eye-in-the-sky technologies. Currently, there is no distinct legislation that prevents ways how AI tools can be misused and monitor people without their consent; especially People of Color, immigrants, or activists of social justice.
Case Study:
In China, facial recognition technology that is in essence an AI application together with other AI-driven technological implementations in surveillance has recently been employed in the repression of the Uyghur Muslim in Xinjiang. AI is a powerful instrument which can be used to bring justice in society but too strict control should be exercised over the applying of AI in surveillance to prevent the infringement of individuals’ rights.
4. Economic Displacement and Inequality
AI implementation in every industry represents one of the principal factors that may significantly increase economic disparity. In the spirit that AI technologies are ever finding ways of reducing automation in some elements of work, this creates a challenge for workers since they are likely to lose their jobs, and result to income-less positions of work. Something as simple as not having the proper supplies can disproportionately hurt these populations: people with low incomes, women, people of color, for example.
Fact:
A report by the McKinsey Global Institute predicted that between 400 and 800 million workers might lose their jobs to automation by 2030. Developing countries are most vulnerable to threat of economic displacement since the labor market is not diversified and most jobs pay very low wages.
Although the integration of AI may lead to development of new employment opportunities and increased efficiency, the process creates challenges for staff that cannot adapt to the new concept of labor market. To overcome these impacts, it is necessary to develop the employees’ skills for using AI applications and adopt proper policies that would distribute the profits of AI introduction evenly throughout the population.
5. Worsening Preexisting Iniquities
AI could also regress the standard of social justice in society, which will be realized through the following social risks.: For instance, if we are designing and implementing the AI systems and we have not considered an opportunity to understanding and meeting the needs of disabled persons or other vulnerable groups, then it means that we are developing general IS and they will not rectify the specific problems affecting disabled persons or other vulnerable persons. AI tools paper that hasn’t incorporated diverse roles would not be conducive to positive social change but rather may foster a culture of inequity between the and the.
To avoid such a situation, AI system development should involve various stakeholders especially the minority ones. Working with civil rights organizations, relevant advocacy groups and leaders will help create technologies that are grounded on fairness and social justice.
AI has a great future in social justice, but changes should be made to its utilization as it regards to aforementioned ethical issues. Therefore, for AI to ensure fairness and equality there is the need to put in place accountability frameworks or transparency, or both, as well as techniques for addressing emerging biases. Ethical AI creation should become a priority before artificial intelligence is going to assist us in reaching social justice goals.
Ensuring Ethical AI Use in Social Justice
This starts by not only identifying opportunities as well as challenges of AI but also finding ways of making sure that AI will not be abused in such a way a; to harm needy individuals in the society. Apart from that implementing strong and well equipped frameworks, accountability, and inclusion are some of the key approaches to ensure that AI can has a positive impact on the social justice cause.
1. Ethical Issues in Artificial Intelligence and Recommendations
Another important way in which proper use of AIs can be secured to support social justice causes to implement proper ethics on AI. Ideally such guidelines should event prevent biases, attract transparency and prevent exploitation. Some organizations have already started initiatives in beginning the construction of regulation models for AI technologies, and the EU has this principle of decency, accountability and discovery at the center of such efforts.
Example: The European Union’s AI Act
Currently, the European Commission is considering an area of AI legislation called the Artificial Intelligence Act for 2021 which seeks to set legal provisions as legal metrics for high risk AI systems. They are among others, requirements concerning decision making procedures, procedures for risk evaluation and guaranteeing that AI is adopted in a way that complies with rights of a human being including right to be treated equally. In turn our governments can use such regulations and guidelines that will define ethical standards for the use of AI for its rightful development ventures that will enrich the society.
However, there is need to develop ethic boards and Artificial Intelligence governance committees that will be responsible for overseeing AI projects that expose vulnerable groups to risks. These boards would make sure that AI apps are designed with social justice in mind and are likewise checked and audited periodically for their ethical effect.
2. Paying attention to to please promote divergence in the development of artificial intelligence.
AI System should be designed by considering a tribute to Society’s marginalized and oppressed in a bid to eliminate social injustices in future use. This is important in order to make certain that AI’s are fair and would be able to solve problems regardless of the target group or demographic.
Smarter Teams Obtain Better Results
Research has also indicated that achieving AI solutions from diverse staff yields higher equality and efficiency. For instance, IBM and Google have worked very hard to ensure that their research on, and MinistWg of, artificial intelligence is gender balanced, and diverse as possible. This if particular important in areas of criminal justice, health care and education where the wrong algorithms may mean a change of life for the victim.
Also, it is important that AI models are developed with concern to cultural and soci-economic realities of societies within which these models will be operated. As a result, for AI developers, it becomes crucial to work with locals, representatives of social justice organizations, and others interested in the plights of minorities to learn about the issues affecting minority populations.
3. This paper proposes that more transparency and accountability mechanisms be implemented in supra-artificial intelligence systems.
AI systems employed in social justice frameworks must be transparent and also properly responsible. This means that these AI models should be able to explain to the stakeholders the decisions that were made and should be flexible for constant analysis. One of the steps in this direction is integrating explainable artificial intelligence into practice.
Explainable AI (XAI)
XAI is the process of making the trained models of machine learning more human sensitive, especially the decision makers, to know how they arrived at a certain decision.
Firstly, but not less important – there should be means for regulation of AI outcomes, as well as punishments for developers or users of AI systems. For instance, if AI systems make decisions that are discriminated or otherwise this harms people, then there should be a power where a person can complain. This could involve legal measures to prevent what is today known as algorithmic bias or filter bias – the way humans don’t discriminate against people based on their race or gender, algorithms should not either.
4. Preprocessing techniques and Fairness Evaluation
Ensuing that AI systems do not in anyway magnify or reinforce bias it is important that the AI models be checked for fairness quite often. The biased elimination entails a procedure of searching for the possible source of bias within the data handling techniques and the algorithms. Bias mitigation strategies include:
- Diverse data collection: The process of collecting and preparing data to teach the AI Algorithms, choosing sample races, genders and income brackets for the model sample.
- Fairness algorithms: Hiring and promoting with fairness in mind; preventing an AI model from being biased by making sure it does not have a witness.
- Continuous monitoring: The Key findings show that Auditing AI systems for fairness is possible and it is valuable to implement an auditing process for systems that incorporates fairness criteria for the AI systems to be audited and create a list of potential improvements to the AI systems.
For instance, Fairness, Accountability, Transparency Conference (FAT/ML) is a gathering that will host researchers and technicians in the field of the improvement of fairness in AI. He stated that through research, dissemination of good practices as well as development of fairness testing frameworks, the above efforts go along way towards increasing social responsibility of AI.
5. Partnership with organizations promoting social justice
Engagement with other SJ organizations, and civil liberties and civil rights organizations is critical to make sure that AI is for the benefit of minorities. Such organizations can give a lot of information regarding the moral and possible issues associated with the use of artificial intelligence in fields such as criminal justice, health and education.
Example: AI and Civil Rights
Currently threepeated criticisms are voiced by The ACLU (American Civil Liberties Union) and The Center for Democracy and Technology, claiming that the application of AI technologies requires intensified regulation of civil liberties. These groups serve a crucial function of tracking AI use as well as of enforcing respect of AI by ensuring that artificial intelligence is developed for and used according to human rights and justice standards.
Also, engaging with CBOs can also ensure that the AI solutions created reflect on the interests of the group who suffers from social justice issues. They can lead to both parties understanding each other better and help the developer come up with an AI solution with little social impact.
Conclusion: AI as a Tool for Lasting Social Change
AI has various implications in social justice: it can become the tool for solving essential problems while at the same time, creating new problems for society. These are the benefits that AI provides: possibility to decrease bias, to provide access to the necessary resources and to improve social services. Nevertheless, the issues of transparency, overt and hidden bias, privacy, accountability, and impartiality or otherwise have to be met front on to make artificial and augmented intelligence qua intelligence be for good.
The steps that will help to make the future of AI journalism more ethical are: creating the ethical guidelines for AI; increasing the inclusiveness; organizing the cooperation with representatives of various spheres and levels, so everyone contributes to the changes for making the AI influence on societies fair and fair. AI in social justice is a beautiful intersection which I believe we need to be very careful as we pursue them so that AI works for the population that needs it most, and not against them.
From this point onwards, we have a task, both individual and collective, to make sure that AI is being created and implemented in a justice, fair and equal manner for all stakeholders from governments, developers, communities, and organizations.
References
- European Commission, Artificial Intelligence Act
European Commission. (2021). Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Retrieved from: https://ec.europa.eu/info/business-economy-euro/banking-and-finance/financial-services-consumer-protection/financial-services-technology/artificial-intelligence_en - COMPAS Algorithm and Criminal Justice
ProPublica. (2016). Machine Bias. ProPublica. Retrieved from: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing - AI and Bias
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). How We Analyzed the COMPAS Recidivism Algorithm. ProPublica. Retrieved from: https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm - AI and Healthcare Equity
Obermeyer, Z., Powers, B. W., Vogeli, C., & Mullainathan, S. (2019). Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342 - AI, Privacy, and Surveillance
Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.
