Category Archives: Coding

Whose side is AI on?

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At the risk of coming across as some sort of Luddite, recent commentary on Artificial Intelligence suggests that it is only natural to have concerns and misgivings about its rapid development and widespread deployment. Of course, at its heart, it’s just another technology at our disposal – but by its very definition, generative AI is not passive, and is likely to impact all areas of our life, whether we invite it in or not.

Over the next few weeks, I will be discussing some non-technical themes relating to AI – creativity and AI, legal implications of AI, and form over substance when it comes to AI itself.

To start with, these are a few of the questions that I have been mulling over:

– Is AI working for us, as a tool that we control and manage?  Or is AI working with us, in a partnership of equals? Or, more likely, is AI working against us, in the sense that it is happening to us, whether we like it or not, let alone whether we are actually aware of it?

– Is AI being wielded by a bunch of tech bros, who feed it with all their own prejudices, unconscious bias and cognitive limitations?

– Who decides what the Large Language Models (LLMs) that power AI are trained on?

– How does AI get permission to create derived content from our own Intellectual Property? Even if our content is on the web, being “publicly available” is not the same as “in the public domain”

– Who is responsible for what AI publishes, and are AI agents accountable for their actions? In the event of false, incorrect, misleading or inappropriate content created by AI, how do we get to clarify the record, or seek a right of reply?

– Why are AI tools adding increased caveats? (“This is not financial advice, this is not to be relied on in a court of law, this is only based on information available as at a certain point in time, this is not a recommendation, etc.”) And is this only going to increase, as in the recent example of changes to Google’s AI-generated search results? (But really, do we need to be told that eating rocks or adding glue to pizza are bad ideas?)

– From my own experience, tools like Chat GPT return “deliberate” factual errors. Why? Is it to keep us on our toes (“Gotcha!”)? Is it to use our responses (or lack thereof) to train the model to be more accurate? Is it to underline the caveat emptor principle (“What, you relied on Otter to write your college essay? What were you thinking?”). Or is it to counter plagiarism (“You could only have got that false information from our AI engine”). If you think the latter is far-fetched, I refer you to the notion of “trap streets” in maps and directories.

– Should AI tools contain better attribution (sources and acknowledgments) in their results? Should they disclose the list of “ingredients” used (like food labelling?) Should they provide verifiable citations for their references? (It’s an idea that is gaining some attention.)

– Finally, the increased use of cloud-based services and crowd-sourced content (not just in AI tools) means that there is the potential for overreach when it comes to end user licensing agreements by ChatGPT, Otter, Adobe Firefly, Gemini, Midjourney etc. Only recently, Adobe had to clarify latest changes to their service agreement, in response to some social media criticism.

Next week: AI and the Human Factor

AI vs IP

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Can Artificial Intelligence software claim copyright in any work that was created using their algorithms?

The short answer is “no”, since only humans can establish copyright in original creative works. Copyright can be assigned to a company or trust, or it can be created under various forms of creative commons, but there still needs to be a human author behind the copyright material. While copyright may lapse over time, it then becomes part of the public domain.

However, the extent to which a human author can claim copyright in a work that has been created with the help of AI is now being challenged. A recent case in the USA has determined that the author of a graphic novel, which included images created using Midjouney, cannot claim copyright in those images. While it was accepted that the author devised the text and other prompts that the software used as the generative inputs, the output images themselves could not be the subject of copyright protection – meaning they are either in the public domain, or they fall under some category of creative commons? This case also indicates that, in the USA at least, failing to declare the use of AI tools in a work when applying for copyright registration may result in a rejected application.

Does this decision mean that the people who write AI programmes could claim copyright in works created using their software? Probably not – as this would imply that Microsoft could establish copyright in every novel written using Word, especially its grammar and spelling tools.

On the other hand, programmers and software developers who use copyright material to train their models may need to obtain relevant permission from the copyright holders (as would anyone using the AI tools and who uses copyright content as prompts), unless they could claim exemptions under “fair dealing” or “fair use” provisions.

We’re still early in the lengthy process whereby copyright and other intellectual property laws are tested and re-calibrated in the wake of AI. Maybe the outcomes of future copyright cases will depend on whether you are Ed Sheeran or Robin Thicke….

Next week: Customer Experience vs Process Design

 

Smart Contracts… or Dumb Software

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The role of smart contracts in blockchain technology is creating an emerging area of jurisprudence which largely overlaps with computer programming. However, one of the first comments I heard about smart contracts when I started working in the blockchain and crypto industry was that they are “neither smart, nor legal”. What does this paradox mean in practice?

First, smart contracts are not “smart”, because they still largely rely on human coders. While self-replicating and self-executing software programs exist, a smart contact contains human-defined parameters or conditions that will trigger the performance of the contract terms once those conditions have been met. The simplest example might be coded as a type of  “if this, then that” function. For example, I could create a smart contract so that every time the temperature drops below 15 degrees, the heating comes on in my house, provided that there is sufficient credit in the digital wallet connected to my utilities billing account.

Second, smart contracts are not “legal”, unless they comprise the necessary elements that form a legally binding agreement: intent, offer, acceptance, consideration, capacity, certainty and legality. They must be capable of being enforceable in the event that one party defaults, but they must not be contrary to public policy, and parties must not have been placed under any form of duress to enter into a contract. Furthermore, there must be an agreed governing law, especially if the parties are in different jurisdictions, and the parties must agree to be subject to a legal venue capable of enforcing or adjudicating the contract in the event of a breach or dispute.

Some legal contacts still need to be in a prescribed form, or in hard copy with a wet signature. A few may need to be under seal or attract stamp duty. Most consumer contracts (and many commercial contracts) are governed by rules relating to unfair contract terms and unconscionable conduct. But assuming a smart contract is capable of being created, notarised and executed entirely on the blockchain, what other legal principles may need to be considered when it comes to capacity and enforcement?

We are all familiar with the process of clicking “Agree” buttons every time we sign up for a social media account, download software or subscribe to digital content. Let’s assume that even with a “free” social media account, there is consideration (i.e., there’s something in it for the consumer in return for providing some personal details), and both parties have the capacity (e.g., they are old enough) and the intent to enter into a contract, the agreement is usually no more than a non-transferable and non-exclusive license granted to the consumer. The license may be revoked at any time, and may even attract penalties in the event of a breach by the end user. There is rarely a transfer of title or ownership to the consumer (if anything, social media platforms effectively acquire the rights to the users’ content), and there is nothing to say that the license will continue into perpetuity. But think how many of these on-line agreements we enter into each day, every time we log into a service or run a piece of software. Soon, those “Agree” buttons could represent individual smart contracts.

When we interact with on-line content, we are generally dealing with a recognised brand or service provider, which represents a known legal entity (a company or corporation). In turn, that entity is capable of entering into a contract, and is also capable of suing/being sued. Legal entities still need to be directed by natural persons (humans) in the form of owners, directors, officers, employees, authorised agents and appointed representatives, who act and perform tasks on behalf of the entity. Where a service provider comprises a highly centralised entity, identifying the responsible party is relatively easy, even if it may require a detailed company search in the case of complex ownership structures and subsidiaries. So what would be the outcome if you entered into a contract with what you thought was an actual person or real company, but it turned out to be an autonmous bot or an instance of disembodied AI – who or what is the counter-party to be held liable in the event something goes awry?

Until DAOs (Decentralised Autonomous Organisations) are given formal legal recognition (including the ability to be sued), it is a grey area as to who may or may not be responsible for the actions of a DAO-based project, and which may be the counter-party to a smart contract. More importantly, who will be responsible for the consequences of the DAO’s actions, once the project is in the community and functioning according to its decentralised rules of self-governance? Some jurisdictions are already drafting laws that will recognise certain DAOs as formal legal entities, which could take the form of a limited liability partnership model or perhaps a particular type of special purpose vehicle. Establishing authority, responsibility and liability will focus on the DAO governance structure: who controls the consensus mechanism, and how do they exercise that control? Is voting to amend the DAO constitution based on proof of stake?

Despite these emerging uncertainties, and the limitations inherent in smart contracts, it’s clear that these programs, where code is increasingly the law, will govern more and more areas of our lives. I see huge potential for smart contracts to be deployed in long-dated agreements such as life insurance policies, home mortgages, pension plans, trusts, wills and estates. These types of legal documents should be capable of evolving dynamically (and programmatically) as our personal circumstances, financial needs and living arrangements also change over time. Hopefully, these smart contracts will also bring greater certainty, clarity and efficiency in the drafting, performance, execution and modification of their terms and conditions.

Next week: Free speech up for sale

 

No-code product development

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Anyone familiar with product development should recognise the image below. It’s a schematic for a start-up idea I was working on several years ago – for an employee engagement, reward and recognition app. It was the result of a number of workshops with a digital agency covering problem statements, user scenarios, workflow solutions, personas, UX/UI design and back-end architecture frameworks.

At the time, the cost quoted to build the MVP was easily 5-6 figures – and even to get to that point still required a load of work on story boards, wire frames and clickable prototypes….

Now, I would expect the developers to use something like a combination of open-source and low-cost software applications to manage the middle-ware functions, dial-up a basic cloud server to host the database and connect to external APIs, and commission a web designer to build a dedicated front-end. (I’m not a developer, programmer or coder, so apologies for any glaring errors in my assumptions…)

The growth in self-serve SaaS platforms, public APIs and low-cost hosting solutions (plus the plethora of design marketplaces) should mean that a developer can build an MVP for a tenth of the cost we were quoted.

Hence the interest in “low-code/no-code” product development, and the use of modular components or stack to build a range of repetitive, automated and small scale applications. (For a dev’s perspective check out Martin Slaney’s article, and for a list of useful resources see Ellen Merryweather’s post from earlier this year.)

There are obvious limitations to this approach: anything too complex, too custom, or which needs to scale quickly may break the model. Equally, stringing together a set of black boxes/off-the-shelf solutions might not work, if there are unforeseen incompatibilities or programming conflicts – especially if one component is upgraded, and there are unknown inter-dependencies that impact the other links in the chain. Which means the product development process will need to ensure a layer of code audits and test environments before deploying into production.

I was reflecting on the benefits and challenges of hermetically sealed operating systems and software programs over the weekend. In trying to downgrade my operating system (so that I could run some legacy third-party applications that no longer work thanks to some recent systems and software “upgrades”), I encountered various challenges, and it took several attempts and a couple of workarounds. The biggest problem was the lack of anything to guide me in advance – that by making certain changes to the system settings, or configuring the software a certain way, either this app or that function wouldn’t work. Also, because each component (the operating system, the software program and the third party applications) wants to defend its own turf within my device, they don’t always play nicely together in a way that the end user wants to deploy them in a single environment.

App interoperability is something that continues to frustrate when it comes to so-called systems or software upgrades. It feels like there needs to be a specialist area of product development that can better identify, mitigate and resolve potential tech debt, as well as navigate the product development maintenance schedule in anticipation of future upgrades and their likely impact, or understand the opportunities for retrofitting and keeping legacy apps current. I see too many app developers abandoning their projects because it’s just too hard to reconfigure for the latest system changes.

Next week: Telstar!