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In the digital age, virtually every aspect of people’s lives is connected through digital channels. On the positive side, this allows instant communication and information access, as well as global connectivity. But this connectivity also introduces a myriad of risks, with cybersecurity threats chief among them.
In such an environment, protecting sensitive information and critical infrastructure has never been more crucial. And yet, the cybersecurity industry is short 4 million workers.
That’s why we invited Tom Vazdar, the program chair of the Master in Enterprise Cybersecurity program at the Open Institute of Technology (OPIT), to shed light on cybersecurity’s critical role in safeguarding our interconnected world. Professor Vazdar will also walk us through the Enterprise Cybersecurity Master’s program at OPIT, explaining what makes it stand out among similar programs.
With extensive experience in various industries (like finance and manufacturing) and countless successful cybersecurity strategies, risk management frameworks, and compliance initiatives under his belt, Professor Vazdar is truly the one to consult. His take on the pressing challenges (and solutions) within the cybersecurity field is invaluable for future students and those already in the industry.
The Current State of Cybersecurity
As Professor Vazdar puts it, “We are living in an era where digital transformation is accelerating.” So, it’s not surprising that new trends (and challenges) continue to emerge in the field. Here’s what Professor Vazdar has to say about them.
Cyberattacks Are Increasing
According to the ISACA’s 2023 State of Cybersecurity report, 48% of organizations reported an increase in cyberattacks compared to the year prior. Professor Vazdar says that this primarily has to do with the increasing complexity of cyberthreats. Simply put, organizations can’t keep up with the escalating sophistication of these threats, resulting in their increased frequency.
But there’s another element to this alarming increase in the number of cyberattacks – a lack of transparency. You see, Professor Vazdar claims that many organizations are believed to underreport cyberattacks. Such underreporting might be due to concerns about reputational damage or regulatory consequences. Either way, it’s exceptionally harmful to the industry, as it hinders the ability to collaborate on developing effective countermeasures and strengthening collective cybersecurity defenses.
Cybersecurity Lacks Workers
As previously mentioned, the cybersecurity industry is experiencing a severe staffing challenge. Interestingly, this doesn’t mean the number of cybersecurity professionals is decreasing. It’s quite the opposite, really.
In 2023, the global cybersecurity workforce grew 8.7% to reach 5.5 million people, a record high. And yet, another 4 million professionals are needed to meet the escalating demand for cybersecurity. If there has ever been a stat to prove just how critical cybersecurity is, this undoubtedly does it.
New Technologies Are Constantly Adopted
Artificial intelligence. Machine learning. Cloud computing. Internet of Things. Blockchain technology. These are just some of the technologies Professor Vazdar singles out as transformative forces reshaping cybersecurity.
On the one hand, these technologies have the power to enhance threat detection and cybersecurity response. On the other, they can also introduce new vulnerabilities and threats, such as data poisoning. The worst part? We’ll let Professor Vazdar explain it:
“All of this has come in a really short period of time, and we, as people, are actually struggling to learn about all these new technologies.”
That’s why he emphasizes the need for continual education in the field, as this is the only way to stay ahead of the curve.
Cybersecurity Strategies Are Becoming Proactive and Predictive
Here’s how it used to be in the cybersecurity world, according to Professor Vazdar: A new massive threat would emerge every few years, affecting the whole world. In the aftermath, you would scramble a team together and work tirelessly for a few days to develop a patch or a solution.
As you can imagine, this approach is hardly viable in today’s oversaturated cybersecurity landscape. That’s why “we’re seeing a shift toward more proactive and more predictive security strategies,” as Professor Vazdar puts it.
Cyberpsychology Is Gaining Importance
Cyberpsychology is by no means a new concept. According to Professor Vazdar, this term was first used in 2008 by Professor Zheng Yan. However, its significance has grown exponentially in recent years. This field of study shifts the focus from the cyberthreat to the cyberattacker.
Its goal is to understand what these malicious actors are doing and why. The result? “We, as humans, know how to defend [ourselves].”
According to Professor Vazdar, this is the third (and most important) layer of defense against cyberthreats. The first concerns the physical environment (i.e., the computer and information systems), while the second is a logical layer that “connects everything together.”
No One Is Immune to Cyberthreats
There’s a common misconception that smaller organizations and individuals aren’t “appealing” to hackers and other malicious actors. However, this couldn’t be further from the truth. No one is immune to cyberthreats, as cybercriminals always have something to gain (regardless of the target’s size or perceived importance).
That’s why investing in cybersecurity is crucial, whether you work for a small IT team or a huge company or just use technology in your day-to-day life.
Why Continuous Education Matters in Cybersecurity
There’s no doubt about it – cybersecurity should be a top priority for everyone in the industry and beyond. But as Professor Vazdar has underscored, what was effective in cybersecurity yesterday might not be sufficient today.
That’s why he emphasizes that “it’s important to get educated [now] more than ever.”
After all, there’s a single constant in the ever-changing cybersecurity field – humans as a crucial line of defense. The more people get educated, the more resilient the protection against cyberthreats becomes.
Why Pursue a Master’s Degree in Cybersecurity at OPIT
One of the postgraduate programs offered by OPIT is the Master of Science (MSc) in Enterprise Cybersecurity. This program is fully remote and can be completed in 12 to 18 months. But enough with the logistics – what makes this program the right choice for getting the much-needed education mentioned above?
Given that he practically shaped this program, Professor Vazdar is the best person to ask this question. He shares with us what makes this program uniquely positioned to prepare students for all the cybersecurity challenges he has touched on in this article.
A Comprehensive Curriculum
According to Professor Vazdar, the first thing that sets this program apart is “the curriculum depth and breadth.” This program covers various topics, from cybersecurity fundamentals (the first module) to advanced areas like AI-driven cybersecurity (the second module).
In other words, this program guarantees two things – a solid cybersecurity foundation and a deep dive into specialized topics. This focus makes it ideal for individuals seeking a well-rounded education in corporate cybersecurity, regardless of their previous experience in the field.
A Unique Structure
Unlike most programs in the industry, OPIT’s Enterprise Cybersecurity program doesn’t solely focus on the technical aspects of cybersecurity. But it doesn’t only dive into the managerial aspect of it either. Instead, it gives you just the “right blend of knowledge,” as Professor Vazdar puts it. Thanks to this approach, you can start working immediately after completing the program. After all, you’re all set skill-wise!
Alignment With Industry Certifications
Industry-standard certifications are becoming increasingly important, as most employers prioritize them when hiring new people. If you’re considering a career in cybersecurity, you’ll be happy to know that OPIT’s Enterprise Cybersecurity program is fully aligned with industry certifications like the Certified Information Systems Security Professional (CISSP). As Professor Vazdar puts it, this ensures that OPIT graduates are “not only academically proficient but that they’re also industry-ready.”
It’s also important to note that this program is internationally recognized and ECTS-accredited by the European Agency for Higher Education and Accreditation.
An Emphasis on Practical Applications
The Enterprise Cybersecurity program places a strong emphasis on practical applications. After all, this is the only way for OPIT students to be industry-ready upon graduating. That’s why the entire third module of the program is dedicated to a Capstone project, a hands-on endeavor that also serves as your dissertation.
A Supportive Environment
One of the aspects of studying at OPIT we’re most proud of is our carefully crafted support team. From the class coordinator to the career advisors, everyone at OPIT has a single goal – to help you succeed.
To this end, all the professors in the Enterprise Cybersecurity program (and beyond) are either academics or experienced professionals with plenty of valuable insights “from the forefront of cybersecurity.”
This course includes interactive lessons, live lectures, and private mentoring sessions, ensuring you never feel alone or isolated at OPIT.
Unparalleled Flexibility
One of the primary reasons for choosing online studying is its incredible flexibility. But OPIT takes this aspect to another level. Besides dictating your own study pace, OPIT lets you choose from several elective courses, allowing you to tailor your learning to your interests and career goals. Professor Vazdar singles out the following courses as the most appealing in terms of what this article has discussed:
- Behavioral Cybersecurity
- Secure Software Development
- AI-Driven Forensic Analysis in Cybersecurity
Give Yourself a Competitive Edge With OPIT
OPIT’s Master of Science in Enterprise Cybersecurity program does much more than educate students. It also prepares them for the future, allowing them to become leaders in cybersecurity. As Professor Vazdar puts it, “Our graduate students will be well-equipped to tackle current and future cybersecurity challenges in different sectors.” And given just how quickly these challenges evolve, you can’t really put a price on such preparation (and education).
So, get in touch with our team of experts to give yourself a competitive edge in the dynamic field of cybersecurity.
-
From the same author
Read the articleSource:
- Agenda Digitale, published on May 16th, 2025
By Riccardo Ocleppo, Founder and Director of OPIT – Open Institute of Technology
AI ethics requires ongoing commitment. Organizations must integrate guidelines and a corporate culture geared towards responsibility and inclusiveness, preventing negative consequences for individuals and society.
In the world of artificial intelligence, concerns about algorithmic bias are coming to the forefront, calling for a collective effort to promote ethical practices in the development and use of AI.
This implies the need to understand the multiple causes and potential consequences of the biases themselves, identify concrete solutions and recognize the key role of academic institutions in this process.
Bias in AI is a form of injustice, often systemic, that can be embedded in algorithms. Its origins are many, but the main culprit is almost always the data set used to train the models. If this data reflects inequalities or prejudices present in society, the risk is that AI will absorb and reproduce them, consolidating these distortions.
But bias can also manifest itself in the opposite direction. This is what happened some time ago with Google Gemini. The generative AI system developed by Google, in an attempt to ensure greater inclusivity, ended up generating content and images completely disconnected from the reality it was supposed to represent.
Further complicating the picture is the very nature of AI models, which are often characterized by complex algorithms and opaque decision-making processes. This complexity makes it difficult to identify, and therefore correct, biases inherent in the systems.
Ethical Data Management to Reduce Bias in AI
Adopting good data management practices is essential to address these issues. The first step is to ensure that the datasets used for training are diverse and representative. This means actively seeking data that includes a wide variety of demographic, cultural, and social contexts, so as to avoid AI exclusively reproducing existing and potentially biased models.
Alongside data diversification, it is equally important to test models on different demographic groups. Only in this way can latent biases that would otherwise remain invisible be highlighted. Furthermore, promoting transparency in algorithms and decision-making processes is crucial. Transparency allows for critical control and makes all actors involved in the design and use of AI accountable.
Strategies for ethical and responsible artificial intelligence
Building ethical AI is not an isolated action, but an ongoing journey that requires constant attention and updating. This commitment is divided into several fundamental steps. First, ethical guidelines must be defined. Organizations must clearly establish the ethical standards to follow in the development and use of AI, inspired by fundamental values such as fairness, responsibility and transparency. These principles serve as a compass to guide all projects.
It is also essential to include a plurality of perspectives in the development of AI. Multidisciplinary teams, composed of technologists, ethicists, sociologists and representatives of the potentially involved communities, can help prevent and correct biases thanks to the variety of approaches. Last but not least, promote an ethical culture : in addition to establishing rules and composing diverse teams, it is essential to cultivate a corporate culture that places ethics at the center of every project. Only by integrating these values in the DNA of the organization can we ensure that ethics is a founding element of the development of AI.
The consequences of biased artificial intelligence
Ignoring the problem of bias can have serious and unpredictable consequences, with profound impacts on different areas of our lives. From the reinforcement of social inequalities to the loss of trust in AI-based systems, the risk is to fuel skepticism and resistance towards technological innovation. AI, if distorted, can negatively influence crucial decisions in sectors such as healthcare, employment and justice. Think, for example, of loan selection algorithms that unfairly penalize certain categories, or facial recognition software that incorrectly identifies people, with possible legal consequences. These are just some of the situations in which an unethical use of AI can worsen existing inequalities.
University training and research to counter bias in AI
Universities and higher education institutions have a crucial responsibility to address bias and promote ethical practices in AI development. Ethics must certainly be integrated into educational curricula. By including ethics modules in AI and computer science courses, universities can provide new generations of developers with the tools to recognize and address bias, contributing to more equitable and inclusive design. Universities can also be protagonists through research.
Academic institutions, with their autonomy and expertise, can explore the complexities of bias in depth, developing innovative solutions for detecting and mitigating bias. Since the topic of bias is multidimensional in nature, a collaborative approach is needed, thus fostering interdisciplinary collaboration. Universities can create spaces where computer scientists, ethicists, lawyers, and social scientists work together, offering more comprehensive and innovative solutions.
But that’s not all. As places of critical thinking and debate, universities can foster dialogue between developers, policy makers, and citizens through events, workshops, and conferences. This engagement is essential to raise awareness and promote responsible use of AI.
In this direction, several universities have already activated degree courses in artificial intelligence that combine advanced technical skills (in areas such as machine learning, computer vision and natural language processing) with training that is attentive to ethical and human implications.
Academic Opportunities for an Equitable AI Future
More and more universities around the world – including Yale and Oxford – are also creating research departments dedicated to AI and ethics.
The path to ethical AI is complex, but it also represents an opportunity to build a future where technology truly serves the common good.
By recognizing the root causes of bias , adopting responsible data practices, and engaging in ongoing and vigilant development, we can reduce the unintended effects of biased algorithms. In this process, academic institutions – thanks to their expertise and authority – are at the forefront, helping to shape a more equitable and inclusive digital age.
Read the articleSource:
- TechFinancials, published on May 16th, 2025
By Zorina Alliata
Artificial intelligence (AI) used to be the stuff of science fiction. Stories about rogue machines and robot uprisings were once a source of amusement, not anxiety. But over recent years, AI has quietly embedded itself in our daily lives.
From the algorithms behind social media feeds, to the voice assistants managing our calendars. This quiet takeover has become something far louder: fear.
Headlines around AI are often alarmist. Statements such as “AI will take your job”, “AI will end education”, or “AI is dangerous and unregulated” are thrown around regularly. These narratives feed on uncertainty and fuel distrust.
But it doesn’t have to be this way. The hyper-fixation on the never-ending negative aspects of AI is the wrong approach to take. What if AI isn’t the villain? What if, at this stage, it’s simply a child?
AI, in many ways, is still learning. It mimics human behaviour, absorbs language, and forms patterns based on what it sees. Its current capabilities, however powerful they may seem, are not equivalent to human intelligence. It has limitations. It makes mistakes. It can even be manipulated and misled. It reflects our world; flaws and all. In that sense, AI is less an omnipotent force and more in line with a toddler trying to find its way.
And, like any child, it needs guidance.
This is especially evident in education. The emergence of AI tools such as ChatGPT has caused a stir in higher education institutions and universities, sparking fears about plagiarism and the erosion of critical thinking. Some institutions have responded with strict bans, while others have embraced cautious integration. The panic is understandable, but is it misplaced?
Rather than jumping to conclusions, educators should consider shifting the conversation. AI can, in fact, become an ally in learning. Instead of assuming students will cheat, we can teach them to use AI responsibly. Most of us educators can already recognise the signs of AI-generated work: excessive use of numbered lists, repetitive language and poor comparison skills. So why not use this as a teaching opportunity?
Encouraging students to engage with AI critically, understanding what it’s good at, where it falls short, and how to improve its output, can strengthen their own analytical skills. It invites them to become more active participants in their learning, not passive consumers of machine generated answers. Teaching young people how to work with AI is arguably more important than shielding them from it.
Outside the classroom, AI’s impact on the workforce is another growing concern. Stories about AI replacing jobs often dominate the news cycle. But these conversations often ignore a key point: AI is not autonomous. AI needs human designers, engineers, analysts, and ethicists to guide it. For every job that AI may eliminate, others will emerge to support and direct it.
More importantly, there are many things AI simply cannot do. It doesn’t understand nuance, morality or emotion. It can’t make ethical decisions without human input. These aren’t minor gaps, they’re fundamental. That’s why we must stop imagining AI as an unstoppable force and start thinking about how to raise it responsibly.
When considering how to raise our AI child responsibly, we need to acknowledge the issue of the algorithm being biased. Critics often point out that AI reproduces prejudices and errors, and whilst this is true, the source of that bias is us. It is important to remember that AI learns from historical data created by us, much of which reflects deeply ingrained societal inequalities.
Take, for example, mortgage lending in the US, where decades of discriminatory practices have skewed the data. Unless we intervene, AI trained on this information will inevitably reflect those same biases.
That’s not a reason to reject AI. It’s a reason to be more involved in its development, like any good parent. The responsibility lies with us.
Parenting is not about control for control’s sake; it’s about nurturing growth while setting boundaries. AI, like a child, needs feedback, accountability, and care. It will grow, but how it grows is up to us.
It’s tempting to view technology as something that happens to us, rather than something we can shape. But AI doesn’t exist outside of society, it’s a product of our values, decisions, and input. If we treat it as a monster, it may become one. If we treat it as a mirror, it will reflect what we show it. And if we treat it as a child, we may be able to raise it into something better.
So instead of fearmongering, let’s ask ourselves a better question: Are we raising AI correctly?
- Zorina Alliata, Professor of Responsible AI at OPIT– Open Institute of Technology
Read the full article below:
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Related posts
Source:
- Agenda Digitale, published on May 16th, 2025
By Riccardo Ocleppo, Founder and Director of OPIT – Open Institute of Technology
AI ethics requires ongoing commitment. Organizations must integrate guidelines and a corporate culture geared towards responsibility and inclusiveness, preventing negative consequences for individuals and society.
In the world of artificial intelligence, concerns about algorithmic bias are coming to the forefront, calling for a collective effort to promote ethical practices in the development and use of AI.
This implies the need to understand the multiple causes and potential consequences of the biases themselves, identify concrete solutions and recognize the key role of academic institutions in this process.
Bias in AI is a form of injustice, often systemic, that can be embedded in algorithms. Its origins are many, but the main culprit is almost always the data set used to train the models. If this data reflects inequalities or prejudices present in society, the risk is that AI will absorb and reproduce them, consolidating these distortions.
But bias can also manifest itself in the opposite direction. This is what happened some time ago with Google Gemini. The generative AI system developed by Google, in an attempt to ensure greater inclusivity, ended up generating content and images completely disconnected from the reality it was supposed to represent.
Further complicating the picture is the very nature of AI models, which are often characterized by complex algorithms and opaque decision-making processes. This complexity makes it difficult to identify, and therefore correct, biases inherent in the systems.
Ethical Data Management to Reduce Bias in AI
Adopting good data management practices is essential to address these issues. The first step is to ensure that the datasets used for training are diverse and representative. This means actively seeking data that includes a wide variety of demographic, cultural, and social contexts, so as to avoid AI exclusively reproducing existing and potentially biased models.
Alongside data diversification, it is equally important to test models on different demographic groups. Only in this way can latent biases that would otherwise remain invisible be highlighted. Furthermore, promoting transparency in algorithms and decision-making processes is crucial. Transparency allows for critical control and makes all actors involved in the design and use of AI accountable.
Strategies for ethical and responsible artificial intelligence
Building ethical AI is not an isolated action, but an ongoing journey that requires constant attention and updating. This commitment is divided into several fundamental steps. First, ethical guidelines must be defined. Organizations must clearly establish the ethical standards to follow in the development and use of AI, inspired by fundamental values such as fairness, responsibility and transparency. These principles serve as a compass to guide all projects.
It is also essential to include a plurality of perspectives in the development of AI. Multidisciplinary teams, composed of technologists, ethicists, sociologists and representatives of the potentially involved communities, can help prevent and correct biases thanks to the variety of approaches. Last but not least, promote an ethical culture : in addition to establishing rules and composing diverse teams, it is essential to cultivate a corporate culture that places ethics at the center of every project. Only by integrating these values in the DNA of the organization can we ensure that ethics is a founding element of the development of AI.
The consequences of biased artificial intelligence
Ignoring the problem of bias can have serious and unpredictable consequences, with profound impacts on different areas of our lives. From the reinforcement of social inequalities to the loss of trust in AI-based systems, the risk is to fuel skepticism and resistance towards technological innovation. AI, if distorted, can negatively influence crucial decisions in sectors such as healthcare, employment and justice. Think, for example, of loan selection algorithms that unfairly penalize certain categories, or facial recognition software that incorrectly identifies people, with possible legal consequences. These are just some of the situations in which an unethical use of AI can worsen existing inequalities.
University training and research to counter bias in AI
Universities and higher education institutions have a crucial responsibility to address bias and promote ethical practices in AI development. Ethics must certainly be integrated into educational curricula. By including ethics modules in AI and computer science courses, universities can provide new generations of developers with the tools to recognize and address bias, contributing to more equitable and inclusive design. Universities can also be protagonists through research.
Academic institutions, with their autonomy and expertise, can explore the complexities of bias in depth, developing innovative solutions for detecting and mitigating bias. Since the topic of bias is multidimensional in nature, a collaborative approach is needed, thus fostering interdisciplinary collaboration. Universities can create spaces where computer scientists, ethicists, lawyers, and social scientists work together, offering more comprehensive and innovative solutions.
But that’s not all. As places of critical thinking and debate, universities can foster dialogue between developers, policy makers, and citizens through events, workshops, and conferences. This engagement is essential to raise awareness and promote responsible use of AI.
In this direction, several universities have already activated degree courses in artificial intelligence that combine advanced technical skills (in areas such as machine learning, computer vision and natural language processing) with training that is attentive to ethical and human implications.
Academic Opportunities for an Equitable AI Future
More and more universities around the world – including Yale and Oxford – are also creating research departments dedicated to AI and ethics.
The path to ethical AI is complex, but it also represents an opportunity to build a future where technology truly serves the common good.
By recognizing the root causes of bias , adopting responsible data practices, and engaging in ongoing and vigilant development, we can reduce the unintended effects of biased algorithms. In this process, academic institutions – thanks to their expertise and authority – are at the forefront, helping to shape a more equitable and inclusive digital age.
Read the full article below:
Source:
- TechFinancials, published on May 16th, 2025
By Zorina Alliata
Artificial intelligence (AI) used to be the stuff of science fiction. Stories about rogue machines and robot uprisings were once a source of amusement, not anxiety. But over recent years, AI has quietly embedded itself in our daily lives.
From the algorithms behind social media feeds, to the voice assistants managing our calendars. This quiet takeover has become something far louder: fear.
Headlines around AI are often alarmist. Statements such as “AI will take your job”, “AI will end education”, or “AI is dangerous and unregulated” are thrown around regularly. These narratives feed on uncertainty and fuel distrust.
But it doesn’t have to be this way. The hyper-fixation on the never-ending negative aspects of AI is the wrong approach to take. What if AI isn’t the villain? What if, at this stage, it’s simply a child?
AI, in many ways, is still learning. It mimics human behaviour, absorbs language, and forms patterns based on what it sees. Its current capabilities, however powerful they may seem, are not equivalent to human intelligence. It has limitations. It makes mistakes. It can even be manipulated and misled. It reflects our world; flaws and all. In that sense, AI is less an omnipotent force and more in line with a toddler trying to find its way.
And, like any child, it needs guidance.
This is especially evident in education. The emergence of AI tools such as ChatGPT has caused a stir in higher education institutions and universities, sparking fears about plagiarism and the erosion of critical thinking. Some institutions have responded with strict bans, while others have embraced cautious integration. The panic is understandable, but is it misplaced?
Rather than jumping to conclusions, educators should consider shifting the conversation. AI can, in fact, become an ally in learning. Instead of assuming students will cheat, we can teach them to use AI responsibly. Most of us educators can already recognise the signs of AI-generated work: excessive use of numbered lists, repetitive language and poor comparison skills. So why not use this as a teaching opportunity?
Encouraging students to engage with AI critically, understanding what it’s good at, where it falls short, and how to improve its output, can strengthen their own analytical skills. It invites them to become more active participants in their learning, not passive consumers of machine generated answers. Teaching young people how to work with AI is arguably more important than shielding them from it.
Outside the classroom, AI’s impact on the workforce is another growing concern. Stories about AI replacing jobs often dominate the news cycle. But these conversations often ignore a key point: AI is not autonomous. AI needs human designers, engineers, analysts, and ethicists to guide it. For every job that AI may eliminate, others will emerge to support and direct it.
More importantly, there are many things AI simply cannot do. It doesn’t understand nuance, morality or emotion. It can’t make ethical decisions without human input. These aren’t minor gaps, they’re fundamental. That’s why we must stop imagining AI as an unstoppable force and start thinking about how to raise it responsibly.
When considering how to raise our AI child responsibly, we need to acknowledge the issue of the algorithm being biased. Critics often point out that AI reproduces prejudices and errors, and whilst this is true, the source of that bias is us. It is important to remember that AI learns from historical data created by us, much of which reflects deeply ingrained societal inequalities.
Take, for example, mortgage lending in the US, where decades of discriminatory practices have skewed the data. Unless we intervene, AI trained on this information will inevitably reflect those same biases.
That’s not a reason to reject AI. It’s a reason to be more involved in its development, like any good parent. The responsibility lies with us.
Parenting is not about control for control’s sake; it’s about nurturing growth while setting boundaries. AI, like a child, needs feedback, accountability, and care. It will grow, but how it grows is up to us.
It’s tempting to view technology as something that happens to us, rather than something we can shape. But AI doesn’t exist outside of society, it’s a product of our values, decisions, and input. If we treat it as a monster, it may become one. If we treat it as a mirror, it will reflect what we show it. And if we treat it as a child, we may be able to raise it into something better.
So instead of fearmongering, let’s ask ourselves a better question: Are we raising AI correctly?
- Zorina Alliata, Professor of Responsible AI at OPIT– Open Institute of Technology
Read the full article below:
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