Instagram might soon ask for your birthday.

Follow Rich DeMuro onInstagramfor more tech news, tips and tricks.

Facebook says the new question is to create a safer, more private experience for young users. Theyll use the information to weed out content and advertising that might not appropriate for them.

Starting now, Instagram will show a notification asking for your date of birth. You can say no a handful of times, but it might impact your ability to continue using the app.

You might also see a warning screen on a post thats sensitive or graphic if you havent already confirmed your birthday, youll have to enter the information to see the post.

Facebook says they know some people will fib about their date of birth, but they have a solution for that, too. The company has already explained how theyre using artificial intelligence to estimate a users age, especially data scraped from posts that mention Happy Birthday.

Keep in mind, Instagram will only show the new birthday prompt to users that havent previously given their age. If youre curious if youve already shared the information (including through a linked Facebook account), you can go to Instagram > Settings > Account > Personal Information.

Listen to theRich on Techpodcast for answers to your tech questions.

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Instagram will soon ask for your age and use artificial intelligence to detect when youre lying - KTLA

Edge software company IOTech has announced it has entered into a partnership with machine learning company Lotus Labs.

The partnership is set to deliver artificial intelligence and visual inference solutions at the IoT edge. The partnership enables IOTech to integrate Lotus Labs' computer vision technology into its edge software solutions.

According to IIOTech, this combination provides a functionality that is especially useful for companies building intelligent solutions across vertical use cases. These include loss prevention in retail, crowd management in entertainment venues, manufacturing component fault detection, COVID safe-distancing management, and smart safety systems within industrial plants.

The integrated solution will enable the data from conventional sensors and OT endpoints to be combined with the results from the latest AI and video inference technologies to provide a much more accurate real-time operational picture and make smarter decisions from the fusion of data.

"An IoT edge system's ability to obtain immediate local insights from vision-based analytics will revolutionise how businesses create new value through their operations," says Keith Steele, chief executive IOTech.

"Our partnership with Lotus Labs creates new opportunities to provide our customers with advanced AI and visual inference solutions," he says.

IOTech has pilot programs underway at major sporting venues and anticipates soon deploying AI and visual inference solutions for a number of these.

Lotus Labs, based in Arizona, provides visual inference through Padm, its AI platform. Padm will be integrated with IOTechs Edge Xpert to offer a comprehensive solution for computer vision at the edge. The solution will support a range of use cases, including people counting, predictive maintenance, product quality checking and theft detection. All of these increase in accuracy through AI and video inference..

"Growth and advancements in AI, machine learning, computer vision and edge computing are coalescing to disrupt traditional business models," says Anjali Nennelli, founder and CEO, Lotus Labs.

"Our partnership with IOTech is another step forward in this journey to combine advanced technologies at the IoT edge to deliver new value for the enterprise," he says.

The global video analytics market size was valued at $4,102.0 million in 2019, and is projected to reach $21,778.0 million by 2027, registering a CAGR of 22.7% from 2020 to 2027 (source Allied Market Research, April 2021). This growth represents a massive opportunity for vendors such as IOTech and Louts Labs who supply the software technologies that will help drive this adoption.

IOTech's Edge Xpert edge computing platform is supported by a pluggable open architecture for computer vision that allows users to run their AI algorithms and vision models at the edge. Edge Xpert allows users to easily control camera devices, collect video streams and automatically apply AI and vision inference right at the edge. The platform supports deploying models that can include object detection, classification and recognition; it passes the inference results to other services for real-time decision making.

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New IOTech partnership to deliver artificial intelligence and visual inference at the Internet of Things edge - IT Brief New Zealand

The intelligence demonstrated by machines is known as Artificial Intelligence. Artificial Intelligence has grown to be very popular in todays world. It is the simulation of natural intelligence in machines that are programmed to learn and mimic the actions of humans. These machines are able to learn with experience and perform human-like tasks. As technologies such as AI continue to grow, they will have a great impact on our quality of life. Its but natural that everyone today wants to connect with AI technology somehow, may it be as an end-user or pursuing a career in Artificial Intelligence.

To learn more about this domain, check out Great Learnings PG Program in Artificial Intelligence and Machine Learning to upskill. This Artificial Intelligence course will help you learn comprehensive curriculum from a top-ranking global schools and to build job-ready artificial intelligence skills. The program offers a hands-on learning experience with top faculty and dedicated mentor support. On completion, you will receive a Certificate from The University of Texas at Austin. Great Learning Academy also offers Free Online Courses that can help you learn the foundations or the basics of the subject and give you a kick-start in your AI journey.

The short answer to What is Artificial Intelligence is that it depends on who you ask. A layman with a fleeting understanding of technology would link it to robots. Theyd say Artificial Intelligence is a terminator like-figure that can act and think on its own. If you ask about artificial intelligence to an AI researcher, (s)he would say that its a set of algorithms that can produce results without having to be explicitly instructed to do so. And they would all be right. So to summarise, Artificial Intelligence meaning is:

Even if we reach that state where an AI can behave as a human does, how can we be sure it can continue to behave that way? We can base the human-likeness of an AI entity with the:

Lets take a detailed look at how these approaches perform:

The basis of the Turing Test is that the Artificial Intelligence entity should be able to hold a conversation with a human agent. The human agent ideally should not able to conclude that they are talking to an Artificial Intelligence. To achieve these ends, the AI needs to possess these qualities:

As the name suggests, this approach tries to build an Artificial Intelligence model-based on Human Cognition. To distil the essence of the human mind, there are 3 approaches:

The Laws of Thought are a large list of logical statements that govern the operation of our mind. The same laws can be codified and applied to artificial intelligence algorithms. The issues with this approach, because solving a problem in principle (strictly according to the laws of thought) and solving them in practice can be quite different, requiring contextual nuances to apply. Also, there are some actions that we take without being 100% certain of an outcome that an algorithm might not be able to replicate if there are too many parameters.

A rational agent acts to achieve the best possible outcome in its present circumstances.According to the Laws of Thought approach, an entity must behave according to the logical statements. But there are some instances, where there is no logical right thing to do, with multiple outcomes involving different outcomes and corresponding compromises. The rational agent approach tries to make the best possible choice in the current circumstances. It means that its a much more dynamic and adaptable agent.Now that we understand how Artificial Intelligence can be designed to act like a human, lets take a look at how these systems are built.

Building an AI system is a careful process of reverse-engineering human traits and capabilities in a machine, and using its computational prowess to surpass what we are capable of. To understand How Aritificial Intelligence actually works, one needs to deep dive into the various sub domains of Artificial Intelligence and and understand how those domains could be applied into the various fields of the industry. You can also take up an artificial intelligence course that will help you gain a comprehensive understanding.

Not all types of AI all the above fields simultaneously. Different Artificial Intelligence entities are built for different purposes, and thats how they vary. AI can be classified based on Type 1 and Type 2 (Based on functionalities). Heres a brief introduction the first type.

Lets take a detailed look.

This is the most common form of AI that youd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of Artificial Intelligence that exists today. Theyre able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters.

AGI is still a theoretical concept. Its defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.Were still a long way away from building an AGI system. An AGI system would need to comprise of thousands of Artificial Narrow Intelligence systems working in tandem, communicating with each other to mimic human reasoning. Even with the most advanced computing systems and infrastructures, such as Fujitsus K or IBMs Watson, it has taken them 40 minutes to simulate a single second of neuronal activity. This speaks to both the immense complexity and interconnectedness of the human brain, and to the magnitude of the challenge of building an AGI with our current resources.

Were almost entering into science-fiction territory here, but ASI is seen as the logical progression from AGI. An Artificial Super Intelligence (ASI) system would be able to surpass all human capabilities. This would include decision making, taking rational decisions, and even includes things like making better art and building emotional relationships.Once we achieve Artificial General Intelligence, AI systems would rapidly be able to improve their capabilities and advance into realms that we might not even have dreamed of. While the gap between AGI and ASI would be relatively narrow (some say as little as a nanosecond, because thats how fast Artificial Intelligence would learn) the long journey ahead of us towards AGI itself makes this seem like a concept that lays far into the future.

Extensive research in Artificial Intelligence also divides it into two more categories, namely Strong Artificial Intelligence and Weak Artificial Intelligence. The terms were coined byJohn Searle in orderto differentiate the performance levels in different kinds of AI machines. Here are some of the core differences between them.

The purpose of Artificial Intelligence is to aid human capabilities and help us make advanced decisions with far-reaching consequences. Thats the answer from a technical standpoint. From a philosophical perspective, Artificial Intelligence has the potential to help humans live more meaningful lives devoid of hard labour, and help manage the complex web of interconnected individuals, companies, states and nations to function in a manner thats beneficial to all of humanity.Currently, the purpose of Artificial Intelligence is shared by all the different tools and techniques that weve invented over the past thousand years to simplify human effort, and to help us make better decisions. Artificial Intelligence has also been touted as our Final Invention, a creation that would invent ground-breaking tools and services that would exponentially change how we lead our lives, by hopefully removing strife, inequality and human suffering.Thats all in the far future though were still a long way from those kinds of outcomes. Currently, Artificial Intelligence is being used mostly by companies to improve their process efficiencies, automate resource-heavy tasks, and to make business predictions based on hard data rather than gut feelings. As all technology that has come before this, the research and development costs need to be subsidised by corporations and government agencies before it becomes accessible to everyday laymen. To learn more about the purpose of artificial intelligence and where it is used, you can take up an AI course and understand the artificial intelligence course details and upskill today.

AI is used in different domains to give insights into user behaviour and give recommendations based on the data. For example, Googles predictive search algorithm used past user data to predict what a user would type next in the search bar. Netflix uses past user data to recommend what movie a user might want to see next, making the user hooked onto the platform and increase watch time. Facebook uses past data of the users to automatically give suggestions to tag your friends, based on their facial features in their images. AI is used everywhere by large organisations to make an end users life simpler. The uses of Artificial Intelligence would broadly fall under the data processing category, which would include the following:

Theres no doubt in the fact that technology has made our life better. From music recommendations, map directions, mobile banking to fraud prevention, AI and other technologies have taken over. Theres a fine line between advancement and destruction. Theres always two sides to a coin, and that is the case with AI as well. Let us take a look at some advantages of Artificial Intelligence-

Lets take a closer look

As a beginner, here are some of the basic prerequisites that will help get started with the subject.

AI truly has the potential to transform many industries, with a wide range of possible use cases. What all these different industries and use cases have in common, is that they are all data-driven. Since Artificial Intelligence is an efficient data processing system at its core, theres a lot of potential for optimisation everywhere.

Lets take a look at the industries where AI is currently shining.

The field of robotics has been advancing even before AI became a reality. At this stage, artificial intelligence is helping robotics to innovate faster with efficient robots. Robots in AI have found applications across verticals and industries especially in the manufacturing and packaging industries. Here are a few applications of robots in AI:

Jobs in AI have been steadily increasing over the past few years and will continue growing at an accelerating rate. 57% of Indian companies are looking forward to hiring the right talent to match up the Market Sentiment. On average, there has been a 60-70% hike in salaries of aspirants who have successfully transitioned into AI roles. Mumbai stays tall in the competition followed by Bangalore and Chennai. As per research, the demand for AI Jobs have increased but efficient workforce has not been keeping pace with it. As per WEF, 133 million jobs would be created in Artificial Intelligence by the year 2020.

Machine learning is a subset of artificial intelligence (AI) which defines one of the core tenets of Artificial Intelligence the ability to learn from experience, rather than just instructions.Machine Learning algorithms automatically learn and improve by learning from their output. They do not need explicit instructions to produce the desired output. They learn by observing their accessible data sets and compares it with examples of the final output. The examine the final output for any recognisable patterns and would try to reverse-engineer the facets to produce an output.

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Deep Learning concepts are used to teach machines what comes naturally to us humans. Using Deep Learning, a computer model can be taught to run classification acts taking image, text, or sound as an input.Deep Learning is becoming popular as the models are capable of achieving state of the art accuracy. Large labelled data sets are used to train these models along with the neural network architectures.Simply put, Deep Learning is using brain simulations hoping to make learning algorithms efficient and simpler to use. Let us now see what is the difference between Deep Learning and Machine Learning.

As the above image portrays, the three concentric ovals describe DL as a subset of ML, which is also another subset of AI. Therefore, AI is the all-encompassing concept that initially erupted. It was then followed by ML that thrived later, and lastly DL that is now promising to escalate the advances of AI to another level.

A component of Artificial Intelligence, Natural Language Processing is the ability of a machine to understand the human language as it is spoken. The objective of NLP is to understand and decipher the human language to ultimately present with a result. Most of the NLP techniques use machine learning to draw insights from human language.Also Read:Most Promising Roles for Artificial Intelligence in India

Computer Vision is a field of study where techniques are developed enabling computers to see and understand the digital images and videos. The goal of computer vision is to draw inferences from visual sources and apply it towards solving a real-world problem.

There are many applications of Computer Vision today, and the future holds an immense scope.

Neural Network is a series of algorithms that mimic the functioning of the human brain to determine the underlying relationships and patterns in a set of data.Also Read:A Peek into Global Artificial Intelligence Strategies

The concept of Neural Networks has found application in developing trading systems for the finance sector. They also assist in the development of processes such as time-series forecasting, security classification, and credit risk modelling.

As humans, we have always been fascinated by technological changes and fiction, right now, we are living amidst the greatest advancements in our history. Artificial Intelligence has emerged to be the next big thing in the field of technology. Organizations across the world are coming up with breakthrough innovations in artificial intelligence and machine learning. Artificial intelligence is not only impacting the future of every industry and every human being but has also acted as the main driver of emerging technologies like big data, robotics and IoT. Considering its growth rate, it will continue to act as a technological innovator for the foreseeable future. Hence, there are immense opportunities for trained and certified professionals to enter a rewarding career. As these technologies continue to grow, they will have more and more impact on the social setting and quality of life.

Getting certified in AI will give you an edge over the other aspirants in this industry. With advancements such as Facial Recognition, AI in Healthcare, Chat-bots, and more, now is the time to build a path to a successful career in Artificial Intelligence. Virtual assistants have already made their way into everyday life, helping us save time and energy. Self-driving cars by Tech giants like Tesla have already shown us the first step to the future. AI can help reduce and predict the risks of climate change, allowing us to make a difference before its too late. And all of these advancements are only the beginning, theres so much more to come. 133 million new Artificial Intelligence jobs are said to be created by Artificial Intelligence by the year 2022.

Ques. Where is AI used?

Ans. Artificial Intelligence is used across industries globally. Some of the industries which have delved deep in the field of AI to find new applications are E-commerce, Retail, Security and Surveillance. Sports Analytics, Manufacturing and Production, Automotive among others.

Ques. How is AI helping in our life?

Ans. The virtual digital assistants have changed the way w do our daily tasks. Alexa and Siri have become like real humans we interact with each day for our every small and big need. The natural language abilities and the ability to learn themselves without human interference are the reasons they are developing so fast and becoming just like humans in their interaction only more intelligent and faster.

Ques. Is Alexa an AI?

Ans. Yes, Alexa is an Artificial Intelligence that lives among us.

Ques. Is Siri an AI?

Ans. Yes, just like Alexa Siri is also an artificial intelligence that uses advanced machine learning technologies to function.

Ques. Why is AI needed?

Ans. AI makes every process better, faster, and more accurate. It has some very crucial applications too such as identifying and predicting fraudulent transactions, faster and accurate credit scoring, and automating manually intense data management practices. Artificial Intelligence improves the existing process across industries and applications and also helps in developing new solutions to problems that are overwhelming to deal with manually.

Ques. What is artificial intelligence with examples?

Ans. Artificial Intelligence is an intelligent entity that is created by humans. It is capable of performing tasks intelligently without being explicitly instructed to do so. We make use of AI in our daily lives without even realizing it. Spotify, Siri, Google Maps, YouTube, all of these applications make use of AI for their functioning.

Ques. Is AI dangerous?

Ans. Although there are several speculations on AI being dangerous, at the moment, we cannot say that AI is dangerous. It has benefited our lives in several ways.

Ques. What is the goal of AI?

Ans. The basic goal of AI is to enable computers and machines to perform intellectual tasks such as problem solving, decision making, perception, and understanding human communication.

Ques. What are the advantages of AI?

Ans. There are several advantages of artificial intelligence. They are listed below:

Ques. Who invented AI?

Ans. The term Artificial Intelligence was coined John McCarthy. He is considered as the father of AI.

Ques. Is artificial intelligence the future?

Ans. We are currently living in the greatest advancements of Artificial Intelligence in history. It has emerged to be the next best thing in technology and has impacted the future of almost every industry. There is a greater need for professionals in the field of AI due to the increase in demand. According to WEF, 133 million new Artificial Intelligence jobs are said to be created by Artificial Intelligence by the year 2022. Yes, AI is the future.

Ques. What is AI and its application?

Ans. AI has paved its way into various industries today. Be it gaming, or healthcare. AI is everywhere. Did you now that the facial recognition feature on our phones uses AI? Google Maps also makes use of AI in its application, and it is part of our daily life more than we know it. Spam filters on Emails, Voice-to-text features, Search recommendations, Fraud protection and prevention, Ride-sharing applicationsare some of the examples of AI and its application.

Whats your view about the future of Artificial Intelligence? Leave your comments below.

Curious to dig deeper into AI, read our blog on some of the top Artificial Intelligence books.

KickStart your Artificial Intelligence Journey with Great Learning which offers high-rated Artificial Intelligence training with world-class training by industry leaders. Whether youre interested in machine learning, data mining, or data analysis, Great Learning has a course for you!

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What is Artificial Intelligence? How Does AI Work ...

During armed conflict, unequal power relations and structural disadvantages derived from gender dynamics are exacerbated. There has been increased recognition of these dynamics during the last several decades, particularly in the context of sexual and gender-based violence in conflict, as exemplified for example in United Nations Security Council Resolution 1325 on Women, Peace, and Security. Though initiatives like this resolution are a positive advancement towards the recognition of discrimination against women and structural disadvantages that they suffer from during armed conflict, other aspects of armed conflict, including, notably, the use of artificial intelligence (AI) for targeting purposes, have remained resistant to insights related to gender. This is particularly problematic in the operational aspect of international humanitarian law (IHL), which contains rules on targeting in armed conflict.

The Gender Dimensions of Distinction and Proportionality

Some gendered dimensions of the application of IHL have long been recognized, especially in the context of rape and other categories of sexual violence against women occurring during armed conflict. Therefore, a great deal of attention has been paid in relation to ensuring accountability for crimes of sexual violence during times of armed conflict, while other aspects of conflict, such as the operational aspect of IHL, have remained overlooked.

In applying the principle of distinction, which requires distinguishing civilians from combatants (only the latter of which may be the target of a lawful attack), gendered assumptions of who is a threat have often played an important role. In modern warfare, often characterized by asymmetry and urban conflict and where combatants can blend in with the civilian population, some militaries and armed groups have struggled to reliably distinguish civilians. Due to gendered stereotypes of expected behavior of women and men, gender has operated as a de facto qualified identity that supplements the category of civilian. In practice this can mean that, for women to be targeted, IHL requirements are rigorously applied. Yet, in the case of young civilian males, the bar seems to be lower gender considerations, coupled with other factors such as geographical location, expose them to a greater risk of being targeted.

An illustrative example of this application of the principle of distinction is in so-called signature strikes, a subset of drone strikes adopted by the United States outside what it considers to be areas of active hostilities. Signature strikes target persons who are not on traditional battlefields without individually identifying them, but rather based only on patterns of life. According to reports on these strikes, it is sufficient that the persons targeted fit into the category military-aged males, who live in regions where terrorists operate, and whose behavior is assessed to be similar enough to those of terrorists to mark them for death. However, as the organization Article 36 notes, due to the lack of transparency around the use of armed drones in signature strikes, it is difficult to determine in more detail what standards are used by the U.S. government to classify certain individuals as legal targets. According to a New York Times report from May 2012, in counting casualties from armed drone strikes, the U.S. government reportedly recorded all military-age males in a strike zone as combatants [] unless there is explicit intelligence posthumously proving them innocent.

However, once a target is assessed as a valid military objective, the impact of gender is reversed in conducting a proportionality assessment. The principle of proportionality requires ensuring the anticipated harm to civilians and civilian objects is not excessive compared to the anticipated military advantage of an attack. But in assessing the anticipated advantage and anticipated civilian harms, the calculated military advantage can include the expected reduction of the commanders own combatant casualties as an advantage in other words, the actual loss of civilian lives can be offset by the avoidance of prospective military casualties. This creates the de facto result that the lives of combatants, the vast majority of whom are men, are weighed as more important than those of civilians who in a battlefield context, are often disproportionately women. Taking these applications of IHL into account, we can conclude that a gendered dimension is present in the operational aspect of this branch of law.

AI Application of IHL Principles

New technologies, particularly AI, have been increasingly deployed to assist commanders in their targeting decisions. Specifically, machine-learning algorithms are being used to process massive amounts of data to identify rules or patterns, drawing conclusions about individual pieces of information based on these patterns. In warfare, AI already supports targeting decisions in various forms. For instance, AI algorithms can estimate collateral damage, thereby helping commanders undertake the proportionality analysis. Likewise, some drones have been outfitted with AI to conduct image-recognition and are currently being trained to scan urban environments to find hidden attackers in other words, to distinguish between civilians and combatants as required by the principle of distinction.

Indeed, in modern warfare, the use of AI is expanding. For example, in March 2021 the National Security Commission on AI, a U.S. congressionally-mandated commission, released a report highlighting how, in the future, AI-enabled technologies are going to permeate every facet of warfighting. It also urged the Department of Defense to integrate AI into critical functions and existing systems in order to become an AI-ready force by 2025. As Neil Davison and Jonathan Horowitz note, as the use of AI grows, it is crucial to ensure that its development and deployment (especially when coupled with the use of autonomous weapons) complies with civilian protection.

Yet even if IHL principles can be translated faithfully into the programming of AI-assisted military technologies (a big and doubtful if), such translation will reproduce or even magnify the disparate, gendered impacts of IHL application identified previously. As the case of drones used to undertake signature strikes demonstrates, the integration of new technologies in warfare risks importing, and in the case of AI tech, potentially magnifying and cementing, the gendered injustices already embodied in the application of existing law.

Gendering Artificial Intelligence-Assisted Warfare

There are several reasons that AI may end up reifying and magnifying gender inequities. First, the algorithms are only as good as their inputs and those underlying data are problematic. To properly work, AI needs massive amounts of data. However, neither the collection nor selection of these data are neutral. In less deadly application domains, such as in mortgage loan decisions or predictive policing, there have been demonstrated instances of gender (and other) biases of both the programmers and the individuals tasked with classifying data samples, or even the data sets themselves (which often contain more data on white, male subjects).

Perhaps even more difficult to identify and correct than individuals biases are instances of machine learning that replicate and reinforce historical patterns of injustice merely because those patterns appear, to the AI, to provide useful information rather than undesirable noise. As Noel Sharkey notes, the societal push towards greater fairness and justice is being held back by historical values about poverty, gender and ethnicity that are ossified in big data. There is no reason to believe that bias in targeting data would be any different or any easier to find.

This means that historical human biases can and do lead to incomplete or unrepresentative training data. For example, a predictive algorithm used to apply the principle of distinction on the basis of target profiles, together with other intelligence, surveillance, and reconnaissance tools, will be gender biased if the data inserted equate military-aged men with combatants and disregard other factors. As the practice of signature drone strikes has demonstrated, automatically classifying men as combatants and women as vulnerable has led to mistakes in targeting. As the use of machine learning in targeting expands, these biases will be amplified if not corrected for with each strike providing increasingly biased data.

To mitigate this result, it is critical to ensure that the data collected are diverse, accurate, and disaggregated, and that algorithm designers reflect on how the principles of distinction and proportionality can be applied in gender-biased ways. High quality data collection means, among other things, ensuring that the data are disaggregated by gender otherwise it will be impossible to learn what biases are operating behind the assumptions used, what works to counter those biases, and what does not.

Ensuring high quality data also requires collecting more and different types of data, including data on women. In addition, because AI tools reflect the biases of those who build them, ensuring that female employees hold technical roles and that male employees are fully trained to understand gender and other biases is also crucial to mitigate data biases. Incorporating gender advisors would also be a positive step to ensure that the design of the algorithm, and the interpretation of what the algorithm recommends or suggests, considers gender biases and dynamics.

However, issues of data quality are subsidiary to larger questions about the possibility of translating IHL into code and, even if this translation is possible, the further difficulty of incorporating gender considerations into IHL code. Encoding gender considerations into AI is challenging to say the least, because gender is both a societal and individual construction. Likewise, the process of developing AI is not neutral, as it has both politics and ethics embedded, as demonstrated by documented incidents of AI encoding biases. Finally, the very rules and principles of modern IHL were drafted when structural discrimination against women was not acknowledged or was viewed as natural or beneficial. As a result, when considering how to translate IHL into code, it is essential to incorporate critical gender perspectives into the interpretation of the norms and laws related to armed conflict.

Gendering IHL: An Early Attempt and Work to be Done

An example of the kind of critical engagement with IHL that will be required is provided by the updated International Committee of the Red Cross (ICRC) Commentary on the Third Geneva Convention. Through the incorporation of particular considerations of gender-specific risks and needs (para. 1747), the updated commentary has reconsidered outdated baseline gender assumptions, such as the idea that women have non-combatant status by default, or that women must receive special consideration because they have less resilience, agency or capacity (para. 1682). This shift has demonstrated that it is not only desirable, but also possible to include a gender perspective in the interpretation of the rules of warfare. This shift also underscores the urgent need to revisit IHL targeting principles of distinction and proportionality to assess how their application impacts genders differently, so that any algorithms developed to execute IHL principles incorporate these insights from the start.

As a first cut at this reexamination, it is essential to reassert that principles of non-discrimination also apply to IHL, and must be incorporated into any algorithmic version of these rules. In particular, the principle of distinction allows commanders to lawfully target only those identified as combatants or those who directly participate in hostilities. Article 50 of Additional Protocol I to the Geneva Conventions defines civilians in a negative way, meaning that civilians are those who do not belong to the category of combatants and IHL makes no reference to gender as a signifier of identity for the purpose of assessing whether a given individual is a combatant. In this regard, being a military-aged male cannot be a shortcut to the identification of combatants. Men make up the category of civilians as well. As Maya Brehm notes, there is scope for categorical targeting within a conduct of hostilities framework, but the principle of non-discrimination continues to apply in armed conflict. Adverse distinction based on race, sex, religion, national origin or similar criteria is prohibited.

Likewise, in any attempt to translate the principle of proportionality into code, there must be recognition of and correction for the gendered impacts of current proportionality calculations. For example, across Syria between 2011 and 2016, 75 percent of the civilian women killed in conflict-related violence were killed by shelling or aerial bombardment. In contrast, 49 percent of civilian men killed in war-related violence were killed by shelling or aerial bombardment; men were killed more often by shooting. This suggests that particular tactics and weapons have disparate impacts on civilian populations that break down along gendered lines. The studys authors note that the evolving tactics used by Syrian, opposition, and international forces in the conflict contributed to a decrease in the proportion of casualties who were combatants, as the use of shelling and bombardment two weapons that were shown to have high rates of civilian casualties, especially women and children civilian casualties increased over time. Study authors also note, however, that changing patterns of civilian and combatant behavior may partially explain the increasing rates of women compared to men in civilian casualties: A possible contributor to increasing proportions of women and children among civilian deaths could be that numbers of civilian men in the population decreased over time as some took up arms to become combatants.

As currently understood, IHL does not require an analysis of the gendered impacts of, for example, the choice of aerial bombardment versus shooting. Yet this research suggests that selecting aerial bombardment as a tactic will result in more civilian women than men being killed (nearly 37 percent of women killed in the conflict versus 23 percent of men). Selecting shooting as a tactic produces opposite results, with 23 percent of civilian men killed by shooting compared to 13 percent of women. There is no right proportion of civilian men and women killed by a given tactic, but these disparities have profound, real-world consequences for civilian populations during and after conflict that are simply not considered under current rules of proportionality and distinction.

In this regard, although using force protection to limit ones own forces casualties is not forbidden, such strategy ought to consider the effect that this policy will have on the civilian population of the opposing side including gendered impacts. The compilation of data on how a certain means or method of warfare may impact the civilian population would enable commanders to take a more informed decision. Acknowledging that the effects of weapons in warfare are gendered is the first key step to be taken. In some cases, there has been progress in incorporating a gendered lens into positive IHL, as in the case of cluster munitions, where Article 5 of the convention banning these weapons notes that States shall provide gender-sensitive assistance to victims. But most of this analysis remains rudimentary and not clearly required. In the context of developing AI-assisted technologies, reflecting on the gendered impact of the algorithm is essential during AI development, acquisition, and application.

The process of encoding IHL principles of distinction and proportionality into AI systems provides a useful opportunity to revisit application of these principles with an eye toward interpretations that take into account modern gender perspectives both in terms of how such IHL principles are interpreted and how their application impacts men and women differently. As the recent update of the ICRC Commentary on the Third Geneva Convention illustrates, acknowledging and incorporating gender-specific needs in the interpretation and suggested application of the existing rules of warfare is not only possible, but also desirable.

Disclaimer:This post has been prepared as part of a research internship at theErasmus University Rotterdam, funded by the European Union (EU) Non-Proliferation and Disarmament Consortium as part of a larger EU educationalinitiative aimed at building capacity in the next generation of scholars and practitioners innon-proliferation policy and programming. The views expressed in this post are those of theauthor and do not necessarily reflect those of the Erasmus University Rotterdam, the EU Non-Proliferation andDisarmament Consortium or other members of the network.

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Embedding Gender in International Humanitarian Law: Is Artificial Intelligence Up to the Task? - Just Security