AI

[AlphaOmega]

That makes sense to me.

Allow AI to remove any inherent bias and allow it to rely on empirical evidence.

Then the short list of candidates can be assessed by humans.

I suppose one potential issue is that presumably we can make a list of jobs for which empathy and other human traits are of lower importance, and presumably these are the roles that AI will fill by itself.

Eventually however, this list might include all jobs...

[M4tt]

That makes sense to me.

Allow AI to remove any inherent bias and allow it to rely on empirical evidence.

Then the short list of candidates can be assessed by humans.

I suppose one potential issue is that presumably we can make a list of jobs for which empathy and other human traits are of lower importance, and presumably these are the roles that AI will fill by itself.

Eventually however, this list might include all jobs...

There's always bias. Take empirical evidence - how do you decide what counts as evidence? How do you decide when evidence conflicts? How do you decide what evidence is relevant and what isn't. If you program these choices in who decides and how do you stop them being biased in the decisions they make. If you provide a more neural style system with training sets, who decide what goes into those training sets. Just for a start.

[Chris_in_the_UK]

There's always bias. Take empirical evidence - how do you decide what counts as evidence? How do you decide when evidence conflicts? How do you decide what evidence is relevant and what isn't. If you program these choices in who decides and how do you stop them being biased in the decisions they make. If you provide a more neural style system with training sets, who decide what goes into those training sets. Just for a start.

Simple.

Score the applications and then do a behavioural interview to explore the evidence.

[M4tt]

Simple.

Score the applications and then do a behavioural interview to explore the evidence.

Cool, against what criteria do you score them?

And what is the difference between a criteria and a bias?

[ryanb741]

Cool, against what criteria do you score them?

And what is the difference between a criteria and a bias?

Ok so I didn't have too many beers and certainly not enough to stop me from coming back to you with a hopefully cogent response.

Specifically with the recruitment function (which works well as an example as our AI stack is aimed at supporting recruitment functions) what is clear is that organisations largely thrive or fail based on the quality of people they employ. And therefore the job of the recruiter is a very important one - they are literally bringing in the fuel to power the business. Great recruiters will have an innate intuition as to how well somebody would fit into their organisation, whether they would thrive, develop and so on. What is also true is that they spend a LOT of time trying to target relevant talent pools and sifting through irrelevant applications. So their true Human intelligence traits are their ability to understand how well someone would fit into their specific organisation, and this is stymied frequently by the sheer amount of admin they have to do.

From our perspective, our aim is to firstly 'decode' talent. What this means is that in the initial process of classifying what someone is from a career perspective, we need to get very, VERY granular. What we refer to as deep taxonomy. For example, typically within the recruitment space, job boards, sites like Linkedin etc will classify someone in IT as 'IT/Software Development'. That's pretty much the depth of the taxonomy they go to. However somebody who is a Ruby on Rails developer doesn't want to see a ton of Java roles when he or she wants to learn about career opportunities. Furthermore, the way we consume digital information has changed and we expect relevant content to be pushed towards us and not have to go out and look for it using imperfect search engines and algorithms. So the answer here is to not get too hung up on what someone is called and focus more on what they do, have done and can do. You can have the job title '4th Duke of Wimbledon' for all we care, what matters is what your digital DNA is.

How does this happen? Well Google in particular holds a lot of information about you. And from our perspective we have a LOT of data around the kinds of things Ruby on Rails developers (as an example) are interested in. We also know that Ruby on Rails developers don't make frequent searches for Ruby On Rails jobs. So we acquire these people on a deep taxonomy profile level from Google, Bing, Facebook (any of the platforms that operate at this level) and you come into our ecosystem which we call a community. We never take a CV as that is totally static data that is out of date the instant it is put together. Once you are in our community and we think you are (in this example) a Ruby on Rails Developer our CRM stack starts sending you content it thinks people with your profile will engage with (using what we refer to as 'intent data'). We see how you engage with it and the frequency of your engagement and these engagements are tailored automatically accordingly. But what is important here is the understanding of what you do engage positively with as this creates a higher 'confidence score' around your profile and the types of things you are interested in. Moreover because we are serving you content based on your profile and digital engagements and not what your job title is we soon learn that like most people from a career perspective you are interested in multiple options. You may be a Ruby on Rails Developer but you also might want to get into teaching, or be a scuba instructor. Your engagements and digital footprint tell us this, plus we know at any given time what your current state of engagement level is and what you engage with so it is always a real time snapshot of what 'floats your boat' at any given time. Again, no way is a simple CV on a database going to tell us that.

Ok so now it gets clever. We run a campaign for client X who wants to target Ruby on Rails Developers. She doesn't want Java Developers applying, she only wants Ruby On Rails Developers applying and it is vital they do so for the future growth of her business. She also knows Ruby on Rails Developers are very difficult to target as they are so scarce and sought after. Because we have built a community globally (in our case of 100m profiles) and have used the aforementioned digital profiling to create a high confidence score about what it is that a user is most interested in at that moment in time we are pretty confident that the people we have classified as Ruby on Rails Developers are indeed Ruby on Rails Developers. As a result of this confidence we run a campaign with a commercial model being that the client pays every time one of these candidates completes an application and is verified as a relevant candidate. So a better store of value for the recruiter than paying for a job listing or some clicks.

Here is where the AI gets involved. When running a campaign, we already know with a high confidence score who is a Ruby on Rails Developer. But we don't know which Ruby on Rails developers are likely to apply for this specific role as everyone is different and will have different motivations. So the AI learns which Ruby on Rails Developer profiles engage best with the recruiter roles, what profile types they share in common and then starts focusing the recruiter adverts more on this higher converting sub profile. What it also does (what we refer to internally as automated SEM by Taxonomy) is go out to Google, Facebook etc and acquire at this very deep taxonomy sub profile level MORE of these profiles if it feels that the profiles already in our communities aren't sufficient to satisfy the client's demands. It also knows what it needs to pay in order to acquire these profiles, how they convert towards the commercial model we have in place with the client and determines if this commercial model needs to change. So the AI has learnt what profiles convert best and are viewed as high quality by the client and takes the decision itself to go out and acquire more of these profiles using the very granular approach we took in the first instance when we acquired the candidates.

The AI drives candidate acquisition based on what it has learned around what our clients need moving forward and what profiles convert best, it learns what content to serve people in our communities (just like Amazon or Netflix serve you different content compared to your peers based on historical engagements you have made on those platforms). This cannot be done by humans at scale and so fast. It isn't cutting human jobs, rather it is enabling humans to focus on doing the tasks that they are best placed to do and drive real impact to their organisations. And it helps organisations pay only for results that matter to their businesses, provides a much better experience for candidates as the content they are after finds them (they don't need to search for it) and the frequency this content is served to them happens based on the frequency that they engage with it.

Whoosh, that was a LOT of information but I hope it made sense (in part at least) :)

[M4tt]

Ok so I didn't have too many beers and certainly not enough to stop me from coming back to you with a hopefully cogent response.

Specifically with the recruitment function (which works well as an example as our AI stack is aimed at supporting recruitment functions) what is clear is that organisations largely thrive or fail based on the quality of people they employ. And therefore the job of the recruiter is a very important one - they are literally bringing in the fuel to power the business. Great recruiters will have an innate intuition as to how well somebody would fit into their organisation, whether they would thrive, develop and so on. What is also true is that they spend a LOT of time trying to target relevant talent pools and sifting through irrelevant applications. So their true Human intelligence traits are their ability to understand how well someone would fit into their specific organisation, and this is stymied frequently by the sheer amount of admin they have to do.

From our perspective, our aim is to firstly 'decode' talent. What this means is that in the initial process of classifying what someone is from a career perspective, we need to get very, VERY granular. What we refer to as deep taxonomy. For example, typically within the recruitment space, job boards, sites like Linkedin etc will classify someone in IT as 'IT/Software Development'. That's pretty much the depth of the taxonomy they go to. However somebody who is a Ruby on Rails developer doesn't want to see a ton of Java roles when he or she wants to learn about career opportunities. Furthermore, the way we consume digital information has changed and we expect relevant content to be pushed towards us and not have to go out and look for it using imperfect search engines and algorithms. So the answer here is to not get too hung up on what someone is called and focus more on what they do, have done and can do. You can have the job title '4th Duke of Wimbledon' for all we care, what matters is what your digital DNA is.

How does this happen? Well Google in particular holds a lot of information about you. And from our perspective we have a LOT of data around the kinds of things Ruby on Rails developers (as an example) are interested in. We also know that Ruby on Rails developers don't make frequent searches for Ruby On Rails jobs. So we acquire these people on a deep taxonomy profile level from Google, Bing, Facebook (any of the platforms that operate at this level) and you come into our ecosystem which we call a community. We never take a CV as that is totally static data that is out of date the instant it is put together. Once you are in our community and we think you are (in this example) a Ruby on Rails Developer our CRM stack starts sending you content it thinks people with your profile will engage with (using what we refer to as 'intent data'). We see how you engage with it and the frequency of your engagement and these engagements are tailored automatically accordingly. But what is important here is the understanding of what you do engage positively with as this creates a higher 'confidence score' around your profile and the types of things you are interested in. Moreover because we are serving you content based on your profile and digital engagements and not what your job title is we soon learn that like most people from a career perspective you are interested in multiple options. You may be a Ruby on Rails Developer but you also might want to get into teaching, or be a scuba instructor. Your engagements and digital footprint tell us this, plus we know at any given time what your current state of engagement level is and what you engage with so it is always a real time snapshot of what 'floats your boat' at any given time. Again, no way is a simple CV on a database going to tell us that.

Ok so now it gets clever. We run a campaign for client X who wants to target Ruby on Rails Developers. She doesn't want Java Developers applying, she only wants Ruby On Rails Developers applying and it is vital they do so for the future growth of her business. She also knows Ruby on Rails Developers are very difficult to target as they are so scarce and sought after. Because we have built a community globally (in our case of 100m profiles) and have used the aforementioned digital profiling to create a high confidence score about what it is that a user is most interested in at that moment in time we are pretty confident that the people we have classified as Ruby on Rails Developers are indeed Ruby on Rails Developers. As a result of this confidence we run a campaign with a commercial model being that the client pays every time one of these candidates completes an application and is verified as a relevant candidate. So a better store of value for the recruiter than paying for a job listing or some clicks.

Here is where the AI gets involved. When running a campaign, we already know with a high confidence score who is a Ruby on Rails Developer. But we don't know which Ruby on Rails developers are likely to apply for this specific role as everyone is different and will have different motivations. So the AI learns which Ruby on Rails Developer profiles engage best with the recruiter roles, what profile types they share in common and then starts focusing the recruiter adverts more on this higher converting sub profile. What it also does (what we refer to internally as automated SEM by Taxonomy) is go out to Google, Facebook etc and acquire at this very deep taxonomy sub profile level MORE of these profiles if it feels that the profiles already in our communities aren't sufficient to satisfy the client's demands. It also knows what it needs to pay in order to acquire these profiles, how they convert towards the commercial model we have in place with the client and determines if this commercial model needs to change. So the AI has learnt what profiles convert best and are viewed as high quality by the client and takes the decision itself to go out and acquire more of these profiles using the very granular approach we took in the first instance when we acquired the candidates.

The AI drives candidate acquisition based on what it has learned around what our clients need moving forward and what profiles convert best, it learns what content to serve people in our communities (just like Amazon or Netflix serve you different content compared to your peers based on historical engagements you have made on those platforms). This cannot be done by humans at scale and so fast. It isn't cutting human jobs, rather it is enabling humans to focus on doing the tasks that they are best placed to do and drive real impact to their organisations. And it helps organisations pay only for results that matter to their businesses, provides a much better experience for candidates as the content they are after finds them (they don't need to search for it) and the frequency this content is served to them happens based on the frequency that they engage with it.

Whoosh, that was a LOT of information but I hope it made sense (in part at least) :)

Sounds cool. I suspect ‘converts’ has a non standard use here. I assume that you continue tracking longitudinally and refine your models. It seems to me that ultimately the trajectories of employees within the firm and even success of the firm would be needed to improve matches and move beyond getting stuck in what people think they want rather than what they need. How do you detect and stop people gaming this system and assess competence rather than interest?

[ryanb741]

Sounds cool. I suspect ‘converts’ has a non standard use here. I assume that you continue tracking longitudinally and refine your models. It seems to me that ultimately the trajectories of employees within the firm and even success of the firm would be needed to improve matches and move beyond getting stuck in what people think they want rather than what they need. How do you detect and stop people gaming this system and assess competence rather than interest?

That's what the recruiter does. Hence AI and Human Intelligence working together. What the AI does is ensure the recruiter is given enough of a talent pool to work with.

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