The text outlines a timeline highlighting the contributions of women and marginalized communities in the field of AI ethics from 2014 to 2023. It emphasizes the historical figures like Ada Lovelace and Katherine Johnson, and the more contemporary efforts by scholars and activists such as Timnit Gebru and Margaret Mitchell, who have challenged biases in AI technologies and advocated for ethical practices. Key events include the publication of influential research papers, protests against corporate practices, and the establishment of initiatives like the AI Bill of Rights. The timeline showcases the ongoing struggle for recognition and equity in AI, urging the acknowledgment of diverse voices and experiences in shaping the technology’s future.
| name | description | change | 10-year | driving-force | relevancy |
|---|---|---|---|---|---|
| Visibility of Women in AI | The contributions of women in AI are becoming more recognized and documented. | From a male-dominated narrative to a more inclusive recognition of women’s contributions. | In 10 years, women and marginalized groups may have more leadership roles and visibility in AI. | Growing movements for gender equality and diversity in technology fields. | 4 |
| Demand for Ethical AI | There is an increasing public demand for ethical considerations in AI development. | From unregulated AI development to a push for ethical standards and accountability. | In a decade, companies may face strict regulations and public scrutiny regarding AI ethics. | Public awareness and activism around the societal impacts of AI technologies. | 5 |
| Algorithmic Accountability | Research and advocacy for accountability in AI algorithms are gaining traction. | From opaque algorithmic processes to calls for transparency and fairness. | In 10 years, there could be established protocols for auditing and regulating AI algorithms. | The growing recognition of biases and harms caused by AI systems. | 5 |
| Community-Led Design Initiatives | Design practices are increasingly being led by marginalized communities. | From top-down design approaches to inclusive, community-driven methodologies. | In a decade, product designs may be more equitable and representative of diverse user needs. | The push for social justice and equity in technology development. | 4 |
| Legislative Actions on AI | Governments are beginning to introduce policies and bills addressing AI issues. | From minimal regulation to proactive legislative frameworks for AI governance. | In 10 years, comprehensive AI regulations may be standard across multiple countries. | Concerns about privacy, bias, and ethical implications of AI technologies. | 5 |
| Whistleblower Protections | Increased legal protections for whistleblowers in tech companies are emerging. | From silencing to empowerment of employees speaking out against unethical practices. | In a decade, industry norms may shift to prioritize ethical reporting and transparency. | The rise of activism and public demand for corporate accountability. | 4 |
| AI Bill of Rights | Discussions around an AI Bill of Rights are gaining momentum in policy circles. | From unregulated AI deployment to the establishment of rights protecting individuals. | In 10 years, an AI Bill of Rights could influence global standards for AI ethics. | The need for protecting individuals against potential harms from AI technologies. | 5 |
| name | description | relevancy |
|---|---|---|
| Racial and Gender Bias in AI | The deployment of AI technology often includes biases that disproportionately affect women and marginalized communities, leading to inequitable outcomes. | 5 |
| Exploitation of Invisible Labor | The technology sector relies on an invisible workforce that is exploited for data labeling and content moderation, raising ethical concerns about labor practices. | 4 |
| Lack of Accountability in AI Deployment | Corporations often prioritize profits over ethical oversight, leading to harmful AI applications with little to no accountability for their consequences. | 5 |
| Privacy Violations through Data Collection | Increased data collection technologies could lead to mass privacy violations, especially as algorithms become more sophisticated. | 5 |
| Regulatory Gaps in AI Governance | Current measures to regulate AI technology are insufficient, risking both ethical and societal implications if not addressed. | 4 |
| Microaggressions in Algorithmic Design | The design and functionality of algorithms often reflect societal biases, perpetuating microaggressions and systemic inequality. | 4 |
| Gendered Censorship by Algorithms | Emerging evidence suggests social media algorithms may suppress content related to women, reflecting deeper societal biases. | 4 |
| Resistance to Ethical AI Practices | Efforts to promote ethical AI practices are often met with resistance from corporations, undermining long-term change in the tech industry. | 5 |
| name | description | relevancy |
|---|---|---|
| Recognition of Diverse Contributions | Increased visibility and acknowledgment of contributions from women and marginalized communities in AI development and ethics. | 5 |
| Advocacy for AI Ethics | Growing movements advocating for ethical AI practices and policies, driven by diverse voices and communities. | 5 |
| Worker Activism in Tech | Rising protests and activism among tech workers, particularly women, against unethical practices in tech companies. | 5 |
| Intersectional Analysis of AI | Focus on intersectionality in AI research, revealing biases in algorithms affecting marginalized groups. | 5 |
| Regulatory Push for AI Accountability | Increasing calls for regulations and accountability in AI development and deployment from various stakeholders. | 4 |
| Community-Driven AI Practices | Emerging practices that prioritize community input and leadership in AI design and implementation. | 4 |
| Public Awareness of Algorithmic Impact | Greater public awareness and discourse surrounding the societal implications of algorithms and AI. | 4 |
| Whistleblowing and Transparency | Encouragement of whistleblowing in tech to expose unethical practices, fostering a culture of transparency. | 4 |
| Development of Ethical AI Frameworks | Creation of frameworks and principles for developing AI responsibly and ethically, emphasizing transparency. | 4 |
| description | relevancy | src |
|---|---|---|
| A mathematically rigorous framework for ensuring privacy in data collection and analysis, developed to address electronic data collection concerns. | 5 | 2b3dffe867d32d85c04baf149e2444ea |
| Formalized safeguards and frameworks to ensure fairness and accuracy in automated predictions and algorithmic decisions. | 5 | 2b3dffe867d32d85c04baf149e2444ea |
| Research highlighting intersectional accuracy disparities in commercial facial recognition systems, focusing on performance for women and dark-skinned individuals. | 5 | 2b3dffe867d32d85c04baf149e2444ea |
| Frameworks aimed at increasing transparency and responsible democratization of machine learning models. | 5 | 2b3dffe867d32d85c04baf149e2444ea |
| A tool for estimating the carbon impact of machine learning processes, addressing environmental concerns in AI development. | 4 | 2b3dffe867d32d85c04baf149e2444ea |
| A framework proposed to protect individuals’ rights in relation to AI technologies and their deployment. | 4 | 2b3dffe867d32d85c04baf149e2444ea |
| Community-led design practices aimed at dismantling structural inequality and advancing collective liberation. | 4 | 2b3dffe867d32d85c04baf149e2444ea |
| Emerging calls for oversight and changes to algorithms used by major tech companies to prioritize user safety and fairness. | 4 | 2b3dffe867d32d85c04baf149e2444ea |
| Research and advocacy around the emotional toll and ethical implications of content moderation in social media. | 4 | 2b3dffe867d32d85c04baf149e2444ea |
| name | description | relevancy |
|---|---|---|
| Visibility of Women and Marginalized Contributors in AI | Recognition of the contributions of women and marginalized communities in AI development, historically overshadowed by male figures. | 5 |
| Ethical AI Development | The need for ethical guidelines and practices in AI development to prevent bias and discrimination in algorithms. | 5 |
| AI and Surveillance Capitalism | Concerns over the implications of AI technologies for privacy, surveillance, and the commodification of personal data. | 4 |
| Racial and Gender Bias in AI Systems | Emerging issues related to biases in AI systems affecting women and racial minorities, impacting fairness and justice. | 5 |
| Labor Exploitation in Tech | The hidden human labor force behind AI systems and its exploitation raises ethical concerns regarding labor rights. | 4 |
| Need for AI Regulation | Growing calls for regulatory frameworks to manage AI risks, including its impact on society and individual rights. | 5 |
| Representation in Tech Leadership | The lack of diversity in tech leadership positions contributes to biased AI outcomes and ethical oversights. | 4 |
| Environmental Impact of AI | The carbon footprint of AI processes emphasizes the need for sustainable practices in technology development. | 4 |
| Community-Led Design in AI | Advocacy for design practices led by marginalized communities to dismantle structural inequality in technology. | 4 |
| Whistleblower Protection in Tech | Increasing recognition of the need for protections for whistleblowers in tech to expose unethical practices. | 4 |