Tag: artificial intelligence ethics

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A Powerful Open Innovator Session That Delivered Game-Changing Insights on AI Ethics

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Events

A Powerful Open Innovator Session That Delivered Game-Changing Insights on AI Ethics

In a recent Open Innovator (OI) Session, ethical considerations in artificial intelligence (AI) development and deployment took center stage. The session convened a multidisciplinary panel to tackle the pressing issues of AI bias, accountability, and governance in today’s fast-paced technological environment.

Details of particpants are are follows:

Moderators:

  • Dr. Akvile Ignotaite- Harvard Univ
  • Naman Kothari– NASSCOM COE

Panelists:

  • Dr. Nikolina Ljepava- AUE
  • Dr. Hamza AGLI– AI Expert, KPMG
  • Betania Allo– Harvard Univ, Founder
  • Jakub Bares– Intelligence Startegist, WHO
  • Dr. Akvile Ignotaite– Harvard Univ, Founder

Featured Innovator:

  • Apurv Garg – Ethical AI Innovation Specialist

The discussion underscored the substantial ethical weight that AI decisions hold, especially in sectors such as recruitment and law enforcement, where AI systems are increasingly prevalent. The diverse panel highlighted the importance of fairness and empathy in system design to serve communities equitably.

AI in Healthcare: A Data Diversity Dilemma

Dr. Aquil Ignotate, a healthcare expert, raised concerns about the lack of diversity in AI datasets, particularly in skin health diagnostics. Studies have shown that these AI models are less effective for individuals with darker skin tones, potentially leading to health disparities. This issue exemplifies the broader challenge of ensuring AI systems are representative of the entire population.

Jacob, from the World Health Organization’s generative AI strategy team, contributed by discussing the data integrity challenge posed by many generative AI models. These models, often designed to predict the next word in a sequence, may inadvertently generate false information, emphasizing the need for careful consideration in their creation and deployment.

Ethical AI: A Strategic Advantage

The panelists argued that ethical AI is not merely a compliance concern but a strategic imperative offering competitive advantages. Trustworthy AI systems are crucial for companies and governments aiming to maintain public confidence in AI-integrated public services and smart cities. Ethical practices can lead to customer loyalty, investment attraction, and sustainable innovation.

They suggested that viewing ethical considerations as a framework for success, rather than constraints on innovation, could lead to more thoughtful and beneficial technological deployment.

Rethinking Accountability in AI

The session addressed the limitations of traditional accountability models in the face of complex AI systems. A shift towards distributed accountability, acknowledging the roles of various stakeholders in AI development and deployment, was proposed. This shift involves the establishment of responsible AI offices and cross-functional ethics councils to guide teams in ethical practices and distribute responsibility among data scientists, engineers, product owners, and legal experts.

AI in Education: Transformation over Restriction

The recent controversies surrounding AI tools like ChatGPT in educational settings were addressed. Instead of banning these technologies, the panelists advocated for educational transformation, using AI as a tool to develop critical thinking and lifelong learning skills. They suggested integrating AI into curricula while educating students on its ethical implications and limitations to prepare them for future leadership roles in a world influenced by AI.

From Guidelines to Governance

The speakers highlighted the gap between ethical principles and practical AI deployment. They called for a transition from voluntary guidelines to mandatory regulations, including ethical impact assessments and transparency measures. These regulations, they argued, would not only protect public interest but also foster innovation by establishing clear development frameworks and fostering public trust.

Importance of Localized Governance

The session stressed the need for tailored regulatory approaches that consider local cultural and legal contexts. This nuanced approach ensures that ethical frameworks are both sustainable and effective in specific implementation environments.

Human-AI Synergy

Looking ahead, the panel envisioned a collaborative future where humans focus on strategic decisions and narratives, while AI handles reporting and information dissemination. This relationship requires maintaining human oversight throughout the AI lifecycle to ensure AI systems are designed to defer to human judgment in complex situations that require moral or emotional understanding.

Practical Insights from the Field

A startup founder from Orava shared real-world challenges in AI governance, such as data leaks resulting from unmonitored machine learning libraries. This underscored the necessity for comprehensive data security and compliance frameworks in AI integration.

AI in Banking: A Governance Success Story

The session touched on AI governance in banking, where monitoring technologies are utilized to track data access patterns and ensure compliance with regulations. These systems detect anomalies, such as unusual data retrieval activities, bolstering security frameworks and protecting customers.

Collaborative Innovation: The Path Forward

The OI Session concluded with a call for government and technology leaders to integrate ethical considerations from the outset of AI development. The conversation highlighted that true ethical AI requires collaboration between diverse stakeholders, including technologists, ethicists, policymakers, and communities affected by the technology.

The session provided a roadmap for creating AI systems that perform effectively and promote societal benefit by emphasizing fairness, transparency, accountability, and human dignity. The future of AI, as outlined, is not about choosing between innovation and ethics but rather ensuring that innovation is ethically driven from its inception.

Write to us at Open-Innovator@Quotients.com/ Innovate@Quotients.com to participate and get exclusive insights.

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Applied Innovation

Unleashing AI’s Promise: Walking the Tightrope Between Bias and Inclusion

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Applied Innovation

Unleashing AI’s Promise: Walking the Tightrope Between Bias and Inclusion

Artificial intelligence (AI) and machine learning have infiltrated almost every facet of contemporary life. Algorithms now underpin many of the decisions that affect our everyday lives, from the streaming entertainment we consume to the recruiting tools used by employers to hire personnel. In terms of equity and inclusiveness, the emergence of AI is a double-edged sword.


On one hand, there is a serious risk that AI systems would perpetuate and even magnify existing prejudices and unfair discrimination against minorities if not built appropriately. On the other hand, if AI is guided in an ethical, transparent, and inclusive manner, technology has the potential to help systematically diminish inequities.

The Risks of Biassed AI


The primary issue is that AI algorithms are not inherently unbiased; they reflect the biases contained in the data used to train them, as well as the prejudices of the humans who create them. Numerous cases have shown that AI may be biased against women, ethnic minorities, and other groups.


One company’s recruitment software was shown to lower candidates from institutions with a higher percentage of female students. Criminal risk assessment systems have shown racial biases, proposing harsher punishments for Black offenders. Some face recognition systems have been criticised for greater mistake rates in detecting women and those with darker complexion.

Debiasing AI for Inclusion.


Fortunately, there is an increasing awareness and effort to create more ethical, fair, and inclusive AI systems. A major focus is on expanding diversity among AI engineers and product teams, as the IT sector is still dominated by white males whose viewpoints might contribute to blind spots.
Initiatives are being implemented to give digital skills training to underrepresented groups. Organizations are also bringing in more female role models, mentors, and inclusive team members to help prevent groupthink.


On the technical side, academics are looking at statistical and algorithmic approaches to “debias” machine learning. One strategy is to carefully curate training data to ensure its representativeness, as well as to check for proxies of sensitive qualities such as gender and ethnicity.

Another is to use algorithmic approaches throughout the modelling phase to ensure that machine learning “fairness” definitions do not result in discriminating outcomes. Tools enable the auditing and mitigation of AI biases.


Transparency around AI decision-making systems is also essential, particularly when utilised in areas such as criminal justice sentencing. The growing area of “algorithmic auditing” seeks to open up AI’s “black boxes” and ensure their fairness.

AI for Social Impact.


In addition to debiasing approaches, AI provides significant opportunity to directly address disparities through creative applications. Digital accessibility tools are one example, with apps that employ computer vision to describe the environment for visually impaired individuals.


In general, artificial intelligence (AI) has “great potential to simplify uses in the digital world and thus narrow the digital divide.” Smart assistants, automated support systems, and personalised user interfaces can help marginalised groups get access to technology.


In the workplace, AI is used to analyse employee data and discover gender/ethnicity pay inequalities that need to be addressed. Smart writing helpers may also check job descriptions for biassed wording and recommend more inclusive phrases to help diversity hiring. Data For Good Volunteer organisations are also using AI and machine intelligence to create social impact initiatives that attempt to reduce societal disparities.


The Path Forward


Finally, AI represents a two-edged sword: it may either aggravate social prejudices and discrimination against minorities, or it can be a strong force for making the world more egalitarian and welcoming. The route forward demands a multi-pronged strategy. Implementing stringent procedures to debias training data and modelling methodologies. Prioritising openness and ensuring justice in AI systems, particularly in high-stakes decision-making. Continued study on AI for social good applications that directly address inequality.

With the combined efforts of engineers, politicians, and society, we can realise AI’s enormous promise as an equalising force for good. However, attention will be required to ensure that these powerful technologies do not exacerbate inequities, but rather contribute to the creation of a more just and inclusive society.

To learn more about AI’s implications and the path to ethical, inclusive AI, contact us at open-innovator@quotients.com.Our team has extensive knowledge of AI bias reduction, algorithmic auditing, and leveraging AI as a force for social good.

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