The EU AI Act categorises AI systems based on their potential impact on safety and fundamental rights. High-risk systems encompass applications in critical sectors, including finance. Notably, AI systems used in credit scoring, fraud prevention, and customer due diligence fall under this category due to their significant influence on individuals' financial well-being.
The EU AI Act categorises AI systems based on their potential impact on safety and fundamental rights. High-risk systems encompass applications in critical sectors, including finance. Notably, AI systems used in credit scoring, fraud prevention, and customer due diligence fall under this category due to their significant influence on individuals' financial well-being.
As organisations navigate today's digital landscape, leveraging artificial intelligence (AI) has become essential for enhancing customer support. According to a report by McKinsey, companies that effectively implement AI in their customer service operations can observe a 40% to 50% reduction in service interactions, and a more than 20% reduction in cost-to-serve.
Ethical considerations in AI revolve around principles such as fairness, transparency, and accountability. AI systems must be designed to avoid biases that could lead to unjust outcomes. For instance, if an AI model is trained on biased data, it may perpetuate existing inequalities, resulting in unfair treatment of individuals or groups. Ensuring transparency allows users to understand and trust AI decisions, while accountability ensures that developers and organisations are held responsible for the outcomes of their AI systems.
The rise of generative AI has brought copyright concerns to the forefront, with major legal battles underscoring the complexity of the issue. The New York Times has filed lawsuits against major AI players like OpenAI, alleging unauthorised use of its articles for training AI models. This illustrates the tension between technological innovation and the protection of original creative work, raising the central legal question: Does using copyrighted material to train AI models qualify as fair use?
Crisis situations escalate rapidly, necessitating immediate actions to mitigate risks and protect stakeholders. Traditional crisis management methods lag behind, struggling to counter misinformation or delays in coordination. Adopting AI mechanisms offers organisations a proactive solution, by anticipating issues before they evolve into larger problems.
The AI in the Maritime Transport sector is projected to reach USD 9.7 billion by 2033, driven by the need for smarter decision-making, enhanced safety, and reduced environmental impact. With its strategic location and world-class infrastructure, Singapore is leading this shift, integrating AI to optimise port operations and streamline logistics while setting a benchmark for the future of intelligent maritime logistics.
A recent report projects that the global AI in the hospitality and tourism market will reach USD 158.40 billion by 2030, growing at a compound annual growth rate of 35.8%. This statistic underscores the financial impact of AI and its role in enhancing operational efficiency and customer satisfaction. To effectively manage this surge in tourism, the adoption of Artificial Intelligence in the hospitality industry is crucial.
According to a Kiplinger article, the IRS plans to use AI to identify potential violations on high-income tax returns, aiming to reduce the substantial $688 billion tax gap. This capability allows tax authorities to analyse vast amounts of data quickly, flagging suspicious activities and ensuring compliance more effectively than ever before.
Google’s Titans is an advanced neural network architecture designed to overcome memory limitations in AI models. Traditional AI, including GPT-4 and LLaMA 3, struggles with retaining long-term context, reducing effectiveness in applications requiring deep reasoning. Titans introduces short-term and long-term memory modules, adaptive learning mechanisms, and scalability beyond 2 million tokens, making it a more efficient and intelligent AI system.
With estimates indicating that data centres consume approximately 4% of global electricity, the Green impact of AI is substantial. This rapidly growing energy consumption raises alarms about climate change and necessitates a shift towards sustainable AI practices. Implementing Green AI policies can help mitigate these effects while promoting innovation and efficiency.
Artificial Intelligence (AI) has become a cornerstone of modern smart home technologies. Devices like smart speakers, thermostats, and security systems leverage AI to automate tasks and enhance user experience. Nearly half of millennials have adopted smart home technologies primarily due to the benefits brought by AI. These innovations empower users to control their environment seamlessly through voice commands or mobile applications. However, these systems are not without flaws.
Artificial Intelligence (AI) is reshaping the recruitment landscape, with nearly 99% of Fortune 500 companies utilising some form of automation in their hiring processes. These systems streamline application reviews and candidate assessments, enhancing efficiency and reducing costs. However, as organisations increasingly adopt these technologies, concerns around algorithmic discrimination and AI bias in hiring have grown. Research shows alarming statistics on AI bias in hiring. AI-powered tools preferred white-associated.
With over 2.2 billion people lacking access to safe drinking water globally, the demand for improved water management systems has never been more urgent. In Europe, where stringent regulations like the EU Water Framework Directive govern water quality and availability, integrating AI into water treatment processes is essential. AI can offer water utilities the ability to predict problems before they occur, optimise the use of limited resources and ensure service reliability, even in the face of climate change and regulatory demands.
Education is evolving rapidly, driven by advancements in Artificial Intelligence. AI in education is projected to grow by $30 billion by 2032. AI-driven education platforms are becoming essential for enhancing learning outcomes and engagement. By harnessing these technologies, educators create more tailored experiences, revolutionising personalised learning.
As artificial intelligence (AI) systems become integral to business operations, understanding the security threats associated with these technologies is essential. AI security encompasses practices designed to protect these systems from unauthorised access, tampering, and malicious attacks. In fact, according to IBM, organisations without AI security face an average data breach cost of USD 5.36 million, which is 18.6% higher than the average cost for all organisations.
AI is expected to contribute $15.7 trillion to the global economy by 2030. From personalised shopping to automated customer support, AI is transforming e-commerce, but are businesses truly prepared for its risks?
Agentic AI has emerged as a transformative force in various industries. A recent survey by Salesforce revealed that 69% of Australian and New Zealand C-suite executives prioritise Agentic AI for innovation over other technologies. Agentic AI emphasises autonomous decision-making and task execution with minimal human intervention.
The global AI in manufacturing market is expected to reach $20.8 billion by 2028, yet its rapid adoption comes with risks—ranging from biased predictive models in supply chains to cybersecurity threats in automated systems. In this context, AI governance, supply chain risk management, and transparency are critical. AI-driven automation improves efficiency, but manufacturers must mitigate risks to avoid production disruptions, unethical sourcing practices, and compliance violations.
While AI holds promise for enhancing operational efficiencies, its burgeoning energy needs can counteract progress toward sustainability. Current projections suggest that AI-related data centres will require more than double their energy consumption by 2026, raising urgent questions about grid reliability and overall sustainability targets.
Organisations face hurdles in ensuring GDPR compliance within their AI projects. Notably, the opaque nature of many machine learning models complicates adherence to transparency requirements. For organisations seeking to deploy AI responsibly while adhering to GDPR regulations, AIQURIS offers comprehensive and automated compliance capabilities.
A successful AI strategy requires comprehensive governance frameworks addressing risks related to bias and accountability. Organisations should implement specific risk management strategies, such as regular audits of AI systems and employee training focused on ethical AI practices. By proactively managing these potential pitfalls, organisations safeguard their reputations and foster stakeholder confidence in their AI initiatives.
A recent study revealed that 96% of law firms in the UK now employ some form of AI technology, showcasing the significant shift towards digital transformation within the industry. As firms seek to enhance efficiency and improve client outcomes, understanding the role of AI in law becomes essential for staying competitive.
Ethical AI deployment requires proactive strategies to address bias, transparency, and accountability. Human-in-the-Loop (HITL) AI is increasingly recognised as a safeguard against unintended consequences, ensuring AI decisions align with ethical and regulatory standards. According to Science Direct, integrating human oversight enhances interpretability and trust, allowing organisations to scale AI responsibly.
As AI technologies become integral to business operations, executive AI literacy is no longer optional. According to Beena Ammanath in Forbes, business leaders recognise that a solid understanding of AI is critical for success over the next five years (Forbes).
AI enhances organisational decision-making capabilities by leveraging machine learning algorithms and neural networks to analyse vast datasets efficiently. According to Intellias, 79% of corporate strategists believe AI will be instrumental in driving business success over the next two years.
According to the International Telecommunication Union, despite substantial advancements in mobile connectivity globally, significant disparities remain—especially for marginalised communities. To effectively address the digital divide, organisations must integrate robust AI governance frameworks that prioritise equitable access.
Did you know that training a single advanced AI model can produce up to 500 tonnes of CO2 emissions? Discover how Green AI solutions can help reduce environmental footprints and align innovation with sustainability.
As AI systems grow more complex, the need for Explainable AI (XAI) becomes critical. Techniques like LIME and SHAP provide transparency into decision-making processes, fostering trust and enabling ethical AI use. But achieving true transparency presents challenges—are you ready to navigate them?
AI risk management is crucial for ensuring AI systems are safe, secure, and compliant. By evaluating six critical pillars—Safety, Security, Legal, Ethics, Performance, and Sustainability—organisations can proactively identify and mitigate risks to drive business success.
The concept of smart cities represents one avenue where AI significantly enhances urban living while addressing environmental concerns. For example, Singapore’s Smart Nation initiative integrates AI-driven systems to monitor energy usage and manage resources efficiently, contributing to reduced emissions and improved livability.
In North America, over 63% of organisations consider AI ethics to be significantly important, highlighting the growing awareness of ethical issues as AI transforms industries. As organisations increasingly rely on AI for crucial decisions, understanding these implications is imperative.
The European Union AI Act is set to reshape the global landscape of artificial intelligence, with penalties for non-compliance reaching up to €35 million. Are you ready to navigate this transformative legislation and safeguard your organisation’s future?
The impact of AI on safety within the oil and gas sector cannot be overstated. With 75% of incidents attributed to human error, AI offers a promising avenue to mitigate these risks. By leveraging accredited AI systems, companies can create safer work environments.
The oil and gas sector is at a pivotal moment as it integrates artificial intelligence (AI) technologies into its operations. In a recent podcast discussion between Andreas Hauser, CEO and co-founder of AIQURIS, and David Mugridge, focused on offshore energy operations, they explored how AI can address safety, governance, and efficiency challenges in this high-risk sector.
AI in the workplace is no longer a futuristic concept; it is a reality that many organisations are beginning to embrace. According to Gallup, 93% of Fortune 500 CHROs report integrating AI tools into their operations. However, only one-third of employees feel aware of these efforts, highlighting a significant gap in communication around AI adoption.
As we step into a new era of technological advancements, artificial intelligence (AI) is becoming increasingly prevalent in public transportation. The integration of AI promises enhanced efficiency and safety but raises significant ethical concerns that must be addressed responsibly.
The future of aviation will undoubtedly be shaped by artificial intelligence, presenting remarkable opportunities alongside substantial challenges. It is imperative to embrace this technology responsibly, ensuring that safety, ethics, and effective governance remain at the forefront of our efforts.
While the benefits of AI in healthcare include enhanced diagnostic accuracy, streamlined operations, and personalised patient care, it also introduces significant risks, such as data breaches, algorithmic bias, and regulatory compliance challenges.
AIQURIS secures pivotal seed funding to lead the charge in responsible AI adoption, ensuring a safer, more ethical future for organizations worldwide.
Dive into our latest blog where we explore how AI is changing the business landscape, offering new opportunities for growth and innovation. Learn about the essential considerations for procuring AI systems, from establishing robust governance to selecting the right provider and ensuring compliance. Discover actionable insights to navigate this transformative journey with confidence.
Camilo Sandoval, former Senior White House Advisor, sheds light on the opaque nature of AI systems. This “black box” nature of AI solutions could be particularly concerning in sectors such as healthcare, finance, law, and government, where transparency is paramount. There is a risk that data biases within these systems can lead to unjust outcomes, such as wrongful allocation of federal benefits.
Camilo Sandoval, former Senior White House Advisor, shares insights on leveraging AI for enterprises’ competitive advantage...
Ethical AI procurement not only fosters trust and transparency among stakeholders, but also ensures sustainable business practices by addressing biases and accountability issues...
In a rapidly evolving AI landscape, enterprises must balance innovation with compliance to data protection laws such as the General Data Protection Regulation (GDPR) and the EU AI Act. GDPR is paramount for EU and EEA enterprises - mandating transparency, accountability, and ethical handling of personal data...
Responsible AI involves an ethical and legal framework ensuring the safe, trustworthy, and fair application of artificial intelligence. It involves governance frameworks, transparency promotion, and accountability measures to mitigate biases and uphold principles of fairness and reliability. Real-world initiatives by companies like Microsoft and IBM exemplify responsible AI adoption through comprehensive principles, tools, and...
As enterprises increasingly rely on third-party AI solutions, it is imperative to adopt a holistic approach to vendor assessments. By integrating AI governance practices and leveraging independent AI risk assessment tools, organisations can navigate the complexities of AI adoption while fostering responsible and ethical AI usage for sustainable business growth
The advent of Artificial Intelligence (AI) brings promise – as well as peril - to industries across Europe. As companies rush to adopt AI technologies to gain a competitive edge, concerns about legal certainty, innovation hindrance, and proper implementation loom large...
Enforcing the EU AI Act involves a multi-faceted approach that integrates regulatory oversight, industry self-regulation, and technological solutions...
With concerted efforts from policymakers, regulators, industry stakeholders, and the broader community, Europe can lead the way in shaping a responsible and ethical future for AI...
In the dynamic landscape of corporate software, recent events surrounding Adobe's Terms of Service (ToS) changes serve as a stark reminder of the evolving risks businesses face, especially with the incorporation of AI. This controversy, while specific to Adobe, underscores broader issues pertinent to AI adoption and highlights the critical need for heightened awareness and proactive risk management strategies within organisations.
How a proactive third-party technology evaluation positioned this autonomous vehicle developer favourably in the procurement process of large automotive manufacturers
AI chatbots provide instant support and seamless engagement, but how reliable are they in practice? Our analysis reveals key gaps in compliance, security, and ethical safeguards that many businesses may be overlooking.
Artificial intelligence (AI) has the potential to revolutionise healthcare by improving diagnostics, treatment options, and operational efficiencies. However, for AI to reach its full potential in this sensitive industry, several critical challenges must be addressed. These challenges include ensuring the availability of high-quality data, adhering to complex regulatory frameworks, and managing the risks inherent in deploying AI systems.
The EU AI Act categorises AI systems based on their potential impact on safety and fundamental rights. High-risk systems encompass applications in critical sectors, including finance. Notably, AI systems used in credit scoring, fraud prevention, and customer due diligence fall under this category due to their significant influence on individuals' financial well-being.
As organisations navigate today's digital landscape, leveraging artificial intelligence (AI) has become essential for enhancing customer support. According to a report by McKinsey, companies that effectively implement AI in their customer service operations can observe a 40% to 50% reduction in service interactions, and a more than 20% reduction in cost-to-serve.
Ethical considerations in AI revolve around principles such as fairness, transparency, and accountability. AI systems must be designed to avoid biases that could lead to unjust outcomes. For instance, if an AI model is trained on biased data, it may perpetuate existing inequalities, resulting in unfair treatment of individuals or groups. Ensuring transparency allows users to understand and trust AI decisions, while accountability ensures that developers and organisations are held responsible for the outcomes of their AI systems.
The rise of generative AI has brought copyright concerns to the forefront, with major legal battles underscoring the complexity of the issue. The New York Times has filed lawsuits against major AI players like OpenAI, alleging unauthorised use of its articles for training AI models. This illustrates the tension between technological innovation and the protection of original creative work, raising the central legal question: Does using copyrighted material to train AI models qualify as fair use?
Crisis situations escalate rapidly, necessitating immediate actions to mitigate risks and protect stakeholders. Traditional crisis management methods lag behind, struggling to counter misinformation or delays in coordination. Adopting AI mechanisms offers organisations a proactive solution, by anticipating issues before they evolve into larger problems.
The AI in the Maritime Transport sector is projected to reach USD 9.7 billion by 2033, driven by the need for smarter decision-making, enhanced safety, and reduced environmental impact. With its strategic location and world-class infrastructure, Singapore is leading this shift, integrating AI to optimise port operations and streamline logistics while setting a benchmark for the future of intelligent maritime logistics.
A recent report projects that the global AI in the hospitality and tourism market will reach USD 158.40 billion by 2030, growing at a compound annual growth rate of 35.8%. This statistic underscores the financial impact of AI and its role in enhancing operational efficiency and customer satisfaction. To effectively manage this surge in tourism, the adoption of Artificial Intelligence in the hospitality industry is crucial.
According to a Kiplinger article, the IRS plans to use AI to identify potential violations on high-income tax returns, aiming to reduce the substantial $688 billion tax gap. This capability allows tax authorities to analyse vast amounts of data quickly, flagging suspicious activities and ensuring compliance more effectively than ever before.
AI chatbots provide instant support and seamless engagement, but how reliable are they in practice? Our analysis reveals key gaps in compliance, security, and ethical safeguards that many businesses may be overlooking.
Google’s Titans is an advanced neural network architecture designed to overcome memory limitations in AI models. Traditional AI, including GPT-4 and LLaMA 3, struggles with retaining long-term context, reducing effectiveness in applications requiring deep reasoning. Titans introduces short-term and long-term memory modules, adaptive learning mechanisms, and scalability beyond 2 million tokens, making it a more efficient and intelligent AI system.
With estimates indicating that data centres consume approximately 4% of global electricity, the Green impact of AI is substantial. This rapidly growing energy consumption raises alarms about climate change and necessitates a shift towards sustainable AI practices. Implementing Green AI policies can help mitigate these effects while promoting innovation and efficiency.
Artificial Intelligence (AI) has become a cornerstone of modern smart home technologies. Devices like smart speakers, thermostats, and security systems leverage AI to automate tasks and enhance user experience. Nearly half of millennials have adopted smart home technologies primarily due to the benefits brought by AI. These innovations empower users to control their environment seamlessly through voice commands or mobile applications. However, these systems are not without flaws.
Artificial Intelligence (AI) is reshaping the recruitment landscape, with nearly 99% of Fortune 500 companies utilising some form of automation in their hiring processes. These systems streamline application reviews and candidate assessments, enhancing efficiency and reducing costs. However, as organisations increasingly adopt these technologies, concerns around algorithmic discrimination and AI bias in hiring have grown. Research shows alarming statistics on AI bias in hiring. AI-powered tools preferred white-associated.
With over 2.2 billion people lacking access to safe drinking water globally, the demand for improved water management systems has never been more urgent. In Europe, where stringent regulations like the EU Water Framework Directive govern water quality and availability, integrating AI into water treatment processes is essential. AI can offer water utilities the ability to predict problems before they occur, optimise the use of limited resources and ensure service reliability, even in the face of climate change and regulatory demands.
Education is evolving rapidly, driven by advancements in Artificial Intelligence. AI in education is projected to grow by $30 billion by 2032. AI-driven education platforms are becoming essential for enhancing learning outcomes and engagement. By harnessing these technologies, educators create more tailored experiences, revolutionising personalised learning.
As artificial intelligence (AI) systems become integral to business operations, understanding the security threats associated with these technologies is essential. AI security encompasses practices designed to protect these systems from unauthorised access, tampering, and malicious attacks. In fact, according to IBM, organisations without AI security face an average data breach cost of USD 5.36 million, which is 18.6% higher than the average cost for all organisations.
AI is expected to contribute $15.7 trillion to the global economy by 2030. From personalised shopping to automated customer support, AI is transforming e-commerce, but are businesses truly prepared for its risks?
Agentic AI has emerged as a transformative force in various industries. A recent survey by Salesforce revealed that 69% of Australian and New Zealand C-suite executives prioritise Agentic AI for innovation over other technologies. Agentic AI emphasises autonomous decision-making and task execution with minimal human intervention.
The global AI in manufacturing market is expected to reach $20.8 billion by 2028, yet its rapid adoption comes with risks—ranging from biased predictive models in supply chains to cybersecurity threats in automated systems. In this context, AI governance, supply chain risk management, and transparency are critical. AI-driven automation improves efficiency, but manufacturers must mitigate risks to avoid production disruptions, unethical sourcing practices, and compliance violations.
While AI holds promise for enhancing operational efficiencies, its burgeoning energy needs can counteract progress toward sustainability. Current projections suggest that AI-related data centres will require more than double their energy consumption by 2026, raising urgent questions about grid reliability and overall sustainability targets.
Organisations face hurdles in ensuring GDPR compliance within their AI projects. Notably, the opaque nature of many machine learning models complicates adherence to transparency requirements. For organisations seeking to deploy AI responsibly while adhering to GDPR regulations, AIQURIS offers comprehensive and automated compliance capabilities.
A successful AI strategy requires comprehensive governance frameworks addressing risks related to bias and accountability. Organisations should implement specific risk management strategies, such as regular audits of AI systems and employee training focused on ethical AI practices. By proactively managing these potential pitfalls, organisations safeguard their reputations and foster stakeholder confidence in their AI initiatives.
A recent study revealed that 96% of law firms in the UK now employ some form of AI technology, showcasing the significant shift towards digital transformation within the industry. As firms seek to enhance efficiency and improve client outcomes, understanding the role of AI in law becomes essential for staying competitive.
Ethical AI deployment requires proactive strategies to address bias, transparency, and accountability. Human-in-the-Loop (HITL) AI is increasingly recognised as a safeguard against unintended consequences, ensuring AI decisions align with ethical and regulatory standards. According to Science Direct, integrating human oversight enhances interpretability and trust, allowing organisations to scale AI responsibly.
As AI technologies become integral to business operations, executive AI literacy is no longer optional. According to Beena Ammanath in Forbes, business leaders recognise that a solid understanding of AI is critical for success over the next five years (Forbes).
AI enhances organisational decision-making capabilities by leveraging machine learning algorithms and neural networks to analyse vast datasets efficiently. According to Intellias, 79% of corporate strategists believe AI will be instrumental in driving business success over the next two years.
According to the International Telecommunication Union, despite substantial advancements in mobile connectivity globally, significant disparities remain—especially for marginalised communities. To effectively address the digital divide, organisations must integrate robust AI governance frameworks that prioritise equitable access.
Did you know that training a single advanced AI model can produce up to 500 tonnes of CO2 emissions? Discover how Green AI solutions can help reduce environmental footprints and align innovation with sustainability.
As AI systems grow more complex, the need for Explainable AI (XAI) becomes critical. Techniques like LIME and SHAP provide transparency into decision-making processes, fostering trust and enabling ethical AI use. But achieving true transparency presents challenges—are you ready to navigate them?
AI risk management is crucial for ensuring AI systems are safe, secure, and compliant. By evaluating six critical pillars—Safety, Security, Legal, Ethics, Performance, and Sustainability—organisations can proactively identify and mitigate risks to drive business success.
The concept of smart cities represents one avenue where AI significantly enhances urban living while addressing environmental concerns. For example, Singapore’s Smart Nation initiative integrates AI-driven systems to monitor energy usage and manage resources efficiently, contributing to reduced emissions and improved livability.
In North America, over 63% of organisations consider AI ethics to be significantly important, highlighting the growing awareness of ethical issues as AI transforms industries. As organisations increasingly rely on AI for crucial decisions, understanding these implications is imperative.
The European Union AI Act is set to reshape the global landscape of artificial intelligence, with penalties for non-compliance reaching up to €35 million. Are you ready to navigate this transformative legislation and safeguard your organisation’s future?
The impact of AI on safety within the oil and gas sector cannot be overstated. With 75% of incidents attributed to human error, AI offers a promising avenue to mitigate these risks. By leveraging accredited AI systems, companies can create safer work environments.
The oil and gas sector is at a pivotal moment as it integrates artificial intelligence (AI) technologies into its operations. In a recent podcast discussion between Andreas Hauser, CEO and co-founder of AIQURIS, and David Mugridge, focused on offshore energy operations, they explored how AI can address safety, governance, and efficiency challenges in this high-risk sector.
AI in the workplace is no longer a futuristic concept; it is a reality that many organisations are beginning to embrace. According to Gallup, 93% of Fortune 500 CHROs report integrating AI tools into their operations. However, only one-third of employees feel aware of these efforts, highlighting a significant gap in communication around AI adoption.
As we step into a new era of technological advancements, artificial intelligence (AI) is becoming increasingly prevalent in public transportation. The integration of AI promises enhanced efficiency and safety but raises significant ethical concerns that must be addressed responsibly.
The future of aviation will undoubtedly be shaped by artificial intelligence, presenting remarkable opportunities alongside substantial challenges. It is imperative to embrace this technology responsibly, ensuring that safety, ethics, and effective governance remain at the forefront of our efforts.
While the benefits of AI in healthcare include enhanced diagnostic accuracy, streamlined operations, and personalised patient care, it also introduces significant risks, such as data breaches, algorithmic bias, and regulatory compliance challenges.
AIQURIS secures pivotal seed funding to lead the charge in responsible AI adoption, ensuring a safer, more ethical future for organizations worldwide.
Artificial intelligence (AI) has the potential to revolutionise healthcare by improving diagnostics, treatment options, and operational efficiencies. However, for AI to reach its full potential in this sensitive industry, several critical challenges must be addressed. These challenges include ensuring the availability of high-quality data, adhering to complex regulatory frameworks, and managing the risks inherent in deploying AI systems.
Dive into our latest blog where we explore how AI is changing the business landscape, offering new opportunities for growth and innovation. Learn about the essential considerations for procuring AI systems, from establishing robust governance to selecting the right provider and ensuring compliance. Discover actionable insights to navigate this transformative journey with confidence.
In the dynamic landscape of corporate software, recent events surrounding Adobe's Terms of Service (ToS) changes serve as a stark reminder of the evolving risks businesses face, especially with the incorporation of AI. This controversy, while specific to Adobe, underscores broader issues pertinent to AI adoption and highlights the critical need for heightened awareness and proactive risk management strategies within organisations.
Camilo Sandoval, former Senior White House Advisor, sheds light on the opaque nature of AI systems. This “black box” nature of AI solutions could be particularly concerning in sectors such as healthcare, finance, law, and government, where transparency is paramount. There is a risk that data biases within these systems can lead to unjust outcomes, such as wrongful allocation of federal benefits.
How a proactive third-party technology evaluation positioned this autonomous vehicle developer favourably in the procurement process of large automotive manufacturers
Camilo Sandoval, former Senior White House Advisor, shares insights on leveraging AI for enterprises’ competitive advantage...
Ethical AI procurement not only fosters trust and transparency among stakeholders, but also ensures sustainable business practices by addressing biases and accountability issues...
In a rapidly evolving AI landscape, enterprises must balance innovation with compliance to data protection laws such as the General Data Protection Regulation (GDPR) and the EU AI Act. GDPR is paramount for EU and EEA enterprises - mandating transparency, accountability, and ethical handling of personal data...
Responsible AI involves an ethical and legal framework ensuring the safe, trustworthy, and fair application of artificial intelligence. It involves governance frameworks, transparency promotion, and accountability measures to mitigate biases and uphold principles of fairness and reliability. Real-world initiatives by companies like Microsoft and IBM exemplify responsible AI adoption through comprehensive principles, tools, and...
As enterprises increasingly rely on third-party AI solutions, it is imperative to adopt a holistic approach to vendor assessments. By integrating AI governance practices and leveraging independent AI risk assessment tools, organisations can navigate the complexities of AI adoption while fostering responsible and ethical AI usage for sustainable business growth
The advent of Artificial Intelligence (AI) brings promise – as well as peril - to industries across Europe. As companies rush to adopt AI technologies to gain a competitive edge, concerns about legal certainty, innovation hindrance, and proper implementation loom large...
Enforcing the EU AI Act involves a multi-faceted approach that integrates regulatory oversight, industry self-regulation, and technological solutions...
With concerted efforts from policymakers, regulators, industry stakeholders, and the broader community, Europe can lead the way in shaping a responsible and ethical future for AI...
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