Ethics & Policy
Navigating the balance between innovation and responsibility
Content provided by Cellebrite
By Jared Barnhart
“Science gathers knowledge faster than society gathers wisdom.
Isaac Asimov, writer and biochemist
In the ever-evolving landscape of law enforcement, technology has consistently played a pivotal role in enhancing the capabilities of police forces worldwide. From the groundbreaking use of fingerprints in the early 20th century to the advent of DNA analysis, each technological milestone has brought both opportunities and challenges. Today, we stand at the frontier of a new era: the age of artificial intelligence (AI) in policing.
As Isaac Asimov aptly noted, “Science gathers knowledge faster than society gathers wisdom.” This observation rings particularly true in the context of AI adoption in law enforcement. While AI promises unprecedented efficiency and insights, it raises critical questions about ethics, privacy and the fundamental relationship between technology and justice.
The evolution of technology in policing
From fingerprints to big data
The journey of technological adoption in law enforcement is a testament to human ingenuity and the relentless pursuit of justice. In 1910, fingerprints were first used to convict a murderer in the United States, marking a revolutionary step in forensic science. As the decades progressed, so did the tools at law enforcement’s disposal.
The introduction of computers in the 1950s laid the groundwork for modern data analysis in policing. A significant milestone came in 1967 with the launch of the National Crime Information Center (NCIC), which transformed how police collected and shared data across jurisdictions.
The digital age and the data explosion
The advent of the internet and mobile technologies in the late 20th and early 21st centuries ushered in an era of unprecedented data generation. Today’s investigators face a deluge of digital evidence from smartphones, social media, surveillance cameras and countless other sources. The average smartphone alone can contain tens of thousands of pieces of data, including call logs, messages, photos and location data.
This explosion of digital information has created both opportunities and challenges for law enforcement. While the wealth of data can provide crucial insights, the sheer volume makes manual analysis impractical, if not impossible.
AI: The next frontier
Artificial intelligence emerges as a powerful solution to navigate this sea of data. AI and its subsets, including machine learning, natural language processing and computer vision, offer the ability to process vast amounts of information at speeds far beyond human capability.
From predictive policing algorithms to facial recognition systems and digital forensics tools, AI is reshaping how law enforcement agencies prevent, investigate and solve crimes. However, with great power comes great responsibility, and the adoption of AI in policing brings with it a host of ethical considerations that must be carefully navigated.
Explore how AI is reshaping digital investigations, tackling complex cases and preparing agencies for tomorrow’s policing challenges
Understanding AI in law enforcement
Defining AI in the context of policing
Artificial intelligence is defined as a “machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments”. In policing, AI manifests in various forms, from algorithms that analyze crime patterns to sophisticated software that can sift through terabytes of digital evidence in minutes.
Applications of AI in modern policing
- Predictive policing: AI algorithms analyze historical crime data to predict potential crime hotspots, allowing for more efficient resource allocation.
- Facial recognition: AI tools can identify individuals in images or video footage, aiding in suspect identification and missing persons cases.
- Digital forensics: AI-powered solutions can aggregate and analyze data from multiple digital sources, creating comprehensive timelines and uncovering hidden connections in complex cases.
- Natural language processing: AI can analyze text from social media, emails and other digital communications to detect potential threats or criminal activity.
- Automated license plate readers: AI-enhanced cameras can scan and process thousands of license plates per minute, alerting officers to stolen vehicles or wanted individuals.
Benefits of AI in investigations and crime prevention
The integration of AI into law enforcement practices offers numerous advantages:
- Efficiency: AI can process vast amounts of data in a fraction of the time it would take human analysts.
- Objectivity: When designed properly and used correctly, AI systems can provide unbiased analysis, free from human prejudices.
- Pattern recognition: AI excels at identifying subtle patterns and connections that might elude human investigators.
- Resource optimization: By automating routine tasks, AI frees up officers to focus on more complex aspects of policing that require human judgment and empathy.
- Proactive policing: Predictive crime algorithms can help agencies allocate resources more effectively, helping to deter crime.
Potential pitfalls and concerns
While the benefits of AI in law enforcement are significant, its adoption is not without risks and ethical concerns. Understanding these potential pitfalls is crucial to be aware of what types of AI solutions are leveraged and how they are implemented.
Misinformation and public perception
The rapid adoption of AI in policing has led to misconceptions and fears among the public:
- Sensationalized media portrayals of AI may create unrealistic expectations or unwarranted fears about its capabilities.
- Lack of public understanding about how AI is used in law enforcement can lead to resistance and distrust.
- Misinformation about AI’s role in policing can hinder productive dialogue about its ethical implementation.
Privacy concerns and data protection
The use of AI in law enforcement may involve processing vast amounts of personal data, raising significant privacy concerns:
- Widespread use of facial recognition technology in public spaces can lead to a sense of constant surveillance.
- The aggregation of data from multiple sources (social media, financial records, location data) may infringe on individuals’ right to privacy.
- There are concerns about data security and the potential for misuse or unauthorized access to sensitive information.
Bias and discrimination risks
One of the most pressing concerns surrounding AI in policing is the potential for bias. AI systems learn from the data provided, and if this data reflects past discriminatory practices or societal biases, the AI may perpetuate or even amplify these biases. For example:
- Predictive policing algorithms trained on historically biased arrest data may disproportionately target minority communities.
- Facial recognition systems could have lower accuracy rates for women and people of color, potentially leading to misidentifications and false arrests.
Transparency and the “black box” problem
AI systems can sometimes feel they operate in “black boxes,” making it difficult to understand how they arrive at their conclusions. This lack of transparency poses several challenges:
- There’s a risk of over-reliance on AI-generated insights without proper human oversight.
- The opacity of AI decision-making processes can erode public trust in law enforcement agencies.
Ensuring ethical AI usage
Common key principles
Addressing the concerns surrounding AI in law enforcement means committing to using AI responsibly, upholding the mission to protect and serve while respecting privacy, offering transparency and fostering public trust in the police and the technology. Model policies and codes of ethics are available from sources ranging from the intelligence community to the United Nations, INTERPOL, NAACP, OECD and Future Policing Institute. Although each varies slightly, they share key principles.
- Do no harm: AI systems should be designed and deployed in ways that respect and protect human rights and fundamental freedoms.
- Fairness and non-discrimination: AI algorithms should be rigorously tested and monitored to ensure they do not perpetuate or amplify biases against individuals or groups.
- Transparency and explainability: Law enforcement agencies should be transparent about their use of AI.
- Privacy protection: Protocols should be in place to protect personal data and ensure compliance with privacy laws and regulations.
- Human oversight: AI should assist in human decision-making, not replace it. Critical decisions should always involve human judgment.
Balancing efficiency and ethics: Example of AI in investigations
One area where AI can significantly aid law enforcement is in investigating digital evidence, given the sheer volume of data and limited time and resources. The ethical use of AI in investigations is paramount. Cellebrite, a leader in digital investigative solutions since 2015, exemplifies how AI can be effectively integrated into investigative processes.
For instance, in a sensitive case involving child sexual exploitation, local police, in collaboration with federal authorities, seized approximately 50 devices containing 35 TB of data. Manually reviewing this data would have taken months. However, Cellebrite’s AI-powered solutions efficiently scanned the data, identifying images, videos and stills that detected nudity and potentially the victim. This process helped investigators narrow down the materials to actionable evidence, acting as an assistant with the human verification. Investigators uncovered 20 child sexual abuse material videos and 17,000 images. This defensible evidence led to a court finding the offender guilty. He was sentenced to 12.5 years in federal prison, thanks in large part to the swift and thorough analysis enabled by AI.
This example underscores how adherence to ethical principles, such as fairness, transparency and human oversight, ensures that AI is used responsibly to protect and serve the community while respecting privacy and fostering public trust.
Conclusion
The integration of AI into law enforcement represents a significant leap forward in the capabilities of police forces worldwide. However, this technological advancement must be balanced with a steadfast commitment to ethical principles and responsible implementation. The journey toward ethical AI in law enforcement is ongoing, requiring continuous evaluation, adaptation and dialogue. As technology evolves, so must our approaches to its ethical use. By maintaining a balance between innovation and responsibility, we can ensure that AI serves as a tool for justice, not a threat to it.
As we navigate this new frontier, it is crucial that law enforcement agencies, policymakers, technology providers and communities work together to ensure that AI enhances public safety while protecting individual rights and freedoms. By adhering to principles of transparency, fairness and accountability, we can harness the power of AI to create a safer and more just society for all.
A note from the sponsor:
Cellebrite is committed to ethical innovation in our AI-powered solutions, which are designed to assist law enforcement in their ethically sound investigations. Cellebrite recognizes the immense responsibility that comes with operating a business that partners with law enforcement agencies while protecting the privacy of citizens. To that end, Cellebrite and our board have a deep commitment to creating a safer world and operating in a lawful and ethical manner that is unwavering.
About the Author
Jared Barnhart is a Customer Success Lead at Cellebrite, a global leader in premier Digital Investigative solutions for the public and private sectors. A former detective and mobile forensics engineer, Jared is highly specialized in digital forensics, regularly training law enforcement and lending his expertise to help them solve cases and accelerate justice.
Ethics & Policy
AI and ethics – what is originality? Maybe we’re just not that special when it comes to creativity?
I don’t trust AI, but I use it all the time.
Let’s face it, that’s a sentiment that many of us can buy into if we’re honest about it. It comes from Paul Mallaghan, Head of Creative Strategy at We Are Tilt, a creative transformation content and campaign agency whose clients include the likes of Diageo, KPMG and Barclays.
Taking part in a panel debate on AI ethics at the recent Evolve conference in Brighton, UK, he made another highly pertinent point when he said of people in general:
We know that we are quite susceptible to confident bullshitters. Basically, that is what Chat GPT [is] right now. There’s something reminds me of the illusory truth effect, where if you hear something a few times, or you say it here it said confidently, then you are much more likely to believe it, regardless of the source. I might refer to a certain President who uses that technique fairly regularly, but I think we’re so susceptible to that that we are quite vulnerable.
And, yes, it’s you he’s talking about:
I mean all of us, no matter how intelligent we think we are or how smart over the machines we think we are. When I think about trust, – and I’m coming at this very much from the perspective of someone who runs a creative agency – we’re not involved in building a Large Language Model (LLM); we’re involved in using it, understanding it, and thinking about what the implications if we get this wrong. What does it mean to be creative in the world of LLMs?
Genuine
Being genuine, is vital, he argues, and being human – where does Human Intelligence come into the picture, particularly in relation to creativity. His argument:
There’s a certain parasitic quality to what’s being created. We make films, we’re designers, we’re creators, we’re all those sort of things in the company that I run. We have had to just face the fact that we’re using tools that have hoovered up the work of others and then regenerate it and spit it out. There is an ethical dilemma that we face every day when we use those tools.
His firm has come to the conclusion that it has to be responsible for imposing its own guidelines here to some degree, because there’s not a lot happening elsewhere:
To some extent, we are always ahead of regulation, because the nature of being creative is that you’re always going to be experimenting and trying things, and you want to see what the next big thing is. It’s actually very exciting. So that’s all cool, but we’ve realized that if we want to try and do this ethically, we have to establish some of our own ground rules, even if they’re really basic. Like, let’s try and not prompt with the name of an illustrator that we know, because that’s stealing their intellectual property, or the labor of their creative brains.
I’m not a regulatory expert by any means, but I can say that a lot of the clients we work with, to be fair to them, are also trying to get ahead of where I think we are probably at government level, and they’re creating their own frameworks, their own trust frameworks, to try and address some of these things. Everyone is starting to ask questions, and you don’t want to be the person that’s accidentally created a system where everything is then suable because of what you’ve made or what you’ve generated.
Originality
That’s not necessarily an easy ask, of course. What, for example, do we mean by originality? Mallaghan suggests:
Anyone who’s ever tried to create anything knows you’re trying to break patterns. You’re trying to find or re-mix or mash up something that hasn’t happened before. To some extent, that is a good thing that really we’re talking about pattern matching tools. So generally speaking, it’s used in every part of the creative process now. Most agencies, certainly the big ones, certainly anyone that’s working on a lot of marketing stuff, they’re using it to try and drive efficiencies and get incredible margins. They’re going to be on the race to the bottom.
But originality is hard to quantify. I think that actually it doesn’t happen as much as people think anyway, that originality. When you look at ChatGPT or any of these tools, there’s a lot of interesting new tools that are out there that purport to help you in the quest to come up with ideas, and they can be useful. Quite often, we’ll use them to sift out the crappy ideas, because if ChatGPT or an AI tool can come up with it, it’s probably something that’s happened before, something you probably don’t want to use.
More Human Intelligence is needed, it seems:
What I think any creative needs to understand now is you’re going to have to be extremely interesting, and you’re going to have to push even more humanity into what you do, or you’re going to be easily replaced by these tools that probably shouldn’t be doing all the fun stuff that we want to do. [In terms of ethical questions] there’s a bunch, including the copyright thing, but there’s partly just [questions] around purpose and fun. Like, why do we even do this stuff? Why do we do it? There’s a whole industry that exists for people with wonderful brains, and there’s lots of different types of industries [where you] see different types of brains. But why are we trying to do away with something that allows people to get up in the morning and have a reason to live? That is a big question.
My second ethical thing is, what do we do with the next generation who don’t learn craft and quality, and they don’t go through the same hurdles? They may find ways to use {AI] in ways that we can’t imagine, because that’s what young people do, and I have faith in that. But I also think, how are you going to learn the language that helps you interface with, say, a video model, and know what a camera does, and how to ask for the right things, how to tell a story, and what’s right? All that is an ethical issue, like we might be taking that away from an entire generation.
And there’s one last ‘tough love’ question to be posed:
What if we’re not special? Basically, what if all the patterns that are part of us aren’t that special? The only reason I bring that up is that I think that in every career, you associate your identity with what you do. Maybe we shouldn’t, maybe that’s a bad thing, but I know that creatives really associate with what they do. Their identity is tied up in what it is that they actually do, whether they’re an illustrator or whatever. It is a proper existential crisis to look at it and go, ‘Oh, the thing that I thought was special can be regurgitated pretty easily’…It’s a terrifying thing to stare into the Gorgon and look back at it and think,’Where are we going with this?’. By the way, I do think we’re special, but maybe we’re not as special as we think we are. A lot of these patterns can be matched.
My take
This was a candid worldview that raised a number of tough questions – and questions are often so much more interesting than answers, aren’t they? The subject of creativity and copyright has been handled at length on diginomica by Chris Middleton and I think Mallaghan’s comments pretty much chime with most of that.
I was particularly taken by the point about the impact on the younger generation of having at their fingertips AI tools that can ‘do everything, until they can’t’. I recall being horrified a good few years ago when doing a shift in a newsroom of a major tech title and noticing that the flow of copy had suddenly dried up. ‘Where are the stories?’, I shouted. Back came the reply, ‘Oh, the Internet’s gone down’. ‘Then pick up the phone and call people, find some stories,’ I snapped. A sad, baffled young face looked back at me and asked, ‘Who should we call?’. Now apart from suddenly feeling about 103, I was shaken by the fact that as soon as the umbilical cord of the Internet was cut, everyone was rendered helpless.
Take that idea and multiply it a billion-fold when it comes to AI dependency and the future looks scary. Human Intelligence matters
Ethics & Policy
Experts gather to discuss ethics, AI and the future of publishing
Publishing stands at a pivotal juncture, said Jeremy North, president of Global Book Business at Taylor & Francis Group, addressing delegates at the 3rd International Conference on Publishing Education in Beijing. Digital intelligence is fundamentally transforming the sector — and this revolution will inevitably create “AI winners and losers”.
True winners, he argued, will be those who embrace AI not as a replacement for human insight but as a tool that strengthens publishing’s core mission: connecting people through knowledge. The key is balance, North said, using AI to enhance creativity without diminishing human judgment or critical thinking.
This vision set the tone for the event where the Association for International Publishing Education was officially launched — the world’s first global alliance dedicated to advancing publishing education through international collaboration.
Unveiled at the conference cohosted by the Beijing Institute of Graphic Communication and the Publishers Association of China, the AIPE brings together nearly 50 member organizations with a mission to foster joint research, training, and innovation in publishing education.
Tian Zhongli, president of BIGC, stressed the need to anchor publishing education in ethics and humanistic values and reaffirmed BIGC’s commitment to building a global talent platform through AIPE.
BIGC will deepen academic-industry collaboration through AIPE to provide a premium platform for nurturing high-level, holistic, and internationally competent publishing talent, he added.
Zhang Xin, secretary of the CPC Committee at BIGC, emphasized that AIPE is expected to help globalize Chinese publishing scholarships, contribute new ideas to the industry, and cultivate a new generation of publishing professionals for the digital era.
Themed “Mutual Learning and Cooperation: New Ecology of International Publishing Education in the Digital Intelligence Era”, the conference also tackled a wide range of challenges and opportunities brought on by AI — from ethical concerns and content ownership to protecting human creativity and rethinking publishing values in higher education.
Wu Shulin, president of the Publishers Association of China, cautioned that while AI brings major opportunities, “we must not overlook the ethical and security problems it introduces”.
Catriona Stevenson, deputy CEO of the UK Publishers Association, echoed this sentiment. She highlighted how British publishers are adopting AI to amplify human creativity and productivity, while calling for global cooperation to protect intellectual property and combat AI tool infringement.
The conference aims to explore innovative pathways for the publishing industry and education reform, discuss emerging technological trends, advance higher education philosophies and talent development models, promote global academic exchange and collaboration, and empower knowledge production and dissemination through publishing education in the digital intelligence era.
yangyangs@chinadaily.com.cn
Ethics & Policy
Experts gather to discuss ethics, AI and the future of publishing
Publishing stands at a pivotal juncture, said Jeremy North, president of Global Book Business at Taylor & Francis Group, addressing delegates at the 3rd International Conference on Publishing Education in Beijing. Digital intelligence is fundamentally transforming the sector — and this revolution will inevitably create “AI winners and losers”.
True winners, he argued, will be those who embrace AI not as a replacement for human insight but as a tool that strengthens publishing”s core mission: connecting people through knowledge. The key is balance, North said, using AI to enhance creativity without diminishing human judgment or critical thinking.
This vision set the tone for the event where the Association for International Publishing Education was officially launched — the world’s first global alliance dedicated to advancing publishing education through international collaboration.
Unveiled at the conference cohosted by the Beijing Institute of Graphic Communication and the Publishers Association of China, the AIPE brings together nearly 50 member organizations with a mission to foster joint research, training, and innovation in publishing education.
Tian Zhongli, president of BIGC, stressed the need to anchor publishing education in ethics and humanistic values and reaffirmed BIGC’s commitment to building a global talent platform through AIPE.
BIGC will deepen academic-industry collaboration through AIPE to provide a premium platform for nurturing high-level, holistic, and internationally competent publishing talent, he added.
Zhang Xin, secretary of the CPC Committee at BIGC, emphasized that AIPE is expected to help globalize Chinese publishing scholarships, contribute new ideas to the industry, and cultivate a new generation of publishing professionals for the digital era.
Themed “Mutual Learning and Cooperation: New Ecology of International Publishing Education in the Digital Intelligence Era”, the conference also tackled a wide range of challenges and opportunities brought on by AI — from ethical concerns and content ownership to protecting human creativity and rethinking publishing values in higher education.
Wu Shulin, president of the Publishers Association of China, cautioned that while AI brings major opportunities, “we must not overlook the ethical and security problems it introduces”.
Catriona Stevenson, deputy CEO of the UK Publishers Association, echoed this sentiment. She highlighted how British publishers are adopting AI to amplify human creativity and productivity, while calling for global cooperation to protect intellectual property and combat AI tool infringement.
The conference aims to explore innovative pathways for the publishing industry and education reform, discuss emerging technological trends, advance higher education philosophies and talent development models, promote global academic exchange and collaboration, and empower knowledge production and dissemination through publishing education in the digital intelligence era.
yangyangs@chinadaily.com.cn
-
Funding & Business7 days ago
Kayak and Expedia race to build AI travel agents that turn social posts into itineraries
-
Jobs & Careers6 days ago
Mumbai-based Perplexity Alternative Has 60k+ Users Without Funding
-
Mergers & Acquisitions6 days ago
Donald Trump suggests US government review subsidies to Elon Musk’s companies
-
Funding & Business6 days ago
Rethinking Venture Capital’s Talent Pipeline
-
Jobs & Careers6 days ago
Why Agentic AI Isn’t Pure Hype (And What Skeptics Aren’t Seeing Yet)
-
Funding & Business4 days ago
Sakana AI’s TreeQuest: Deploy multi-model teams that outperform individual LLMs by 30%
-
Funding & Business7 days ago
From chatbots to collaborators: How AI agents are reshaping enterprise work
-
Jobs & Careers6 days ago
Astrophel Aerospace Raises ₹6.84 Crore to Build Reusable Launch Vehicle
-
Jobs & Careers6 days ago
Telangana Launches TGDeX—India’s First State‑Led AI Public Infrastructure
-
Jobs & Careers4 days ago
Ilya Sutskever Takes Over as CEO of Safe Superintelligence After Daniel Gross’s Exit