Empathy helps us create appealing content interactions and encourage user engagement. Digital assets tagged to empathy concepts increases user interaction with brands throughout the customer journey. But how can we tag at scale for these types of concepts? Is artificial intelligence (AI) the solution, or an impractical approach? Can AI provide appropriate empathy-based tags without bias and with the appropriate context? Rebecca Schneider explored all of these questions at the DAM Practitioners Summit 2020.
Today I want to explore how we can potentially tag for empathy at scale (instead of small one-off projects).
This is an open discussion, and I welcome any comments or questions during this presentation.
I work a lot with taxonomies and metadata with many of my clients.
I’m a content strategist that has worked with digital asset management projects.
So why this presentation?
This sprung from a comment I made during the 2018 DAM Summit in (I believe) this very same room. It was in response regarding the future direction of DAMs, I said that it would be nice to see more empathy represented in asset tagging.
Insight Exchange Network contacted me about speaking at the 2019 Summit – and I spoke on “Tagging and Empathy.”
I posed a question: can we tag to scale for empathy?
Then I had to pull the presentation together! After interviews, much research and thinking, here are my thoughts. This is an exploration and a start of a discussion.
So, again, feel free to contribute!
There are three types of empathy.
Cognitive. Emotional recognition; the perception and accurate identification of the ‘feeling states’ of others.
Emotional. Also called “Affective Empathy” is the mirroring of the ‘feeling states’ of others.
Compassionate. Feelings of sympathy, concern and compassion for another. Often considered to be a consequence of the first two forms of empathy. Typically this type of empathy is the most socially desirable.
A great video explaining empathy, by Brene Brown: https://youtu.be/1Evwgu369Jw
The key here, is that empathy is a skill that can be taught.
Quote: Margaret Magnarelli, https://contently.com/2017/11/03/marketing-buzzword-actually-care-about/
While empathy is considered to be good for people, what about organizations?
Keeping empathy in mind, helps to: Creates distinctive content experiences that many consumers are expecting. These distinctive content experiences lead to brand loyalty. And they increase consumer interest.
So where does all of this talk about empathy get us? Let’s talk about context and think in terms of representing empathy when tagging assets.
This is particularly important for global brands, but can apply to all brands.
Culture – Way of life (which can differ within the same country). As someone born and raised in the Midwest and who has been living on the East coast for 20 years, I can certainly attest to this.
Education – Education level is key here, as well as associations with college or university experiences (especially in sports).
Income – For certain brands, understanding income is important. Is it affordable? Is it exclusive? Is it trendy? Will I look cool?
Ethnicity – Where do you feel at ‘home’? Who are your ‘people’? This can be small or large groups.
Social Norms – What is socially acceptable to you? Again, this could vary widely within a group or geographic area.
Keeping this context in mind, let’s talk metadata.
Now what about empathy metadata? I like to think in terms of an empathy map.
Who is the user and his/her context?
Audience – this is really where the context comes into play. Segment - is the division of the market or population into subgroups with similar motivations. Segments can include: geographic, demographic, use of product, level of expertise Persona - fictional characters (archetypes), which are created based upon research in order to represent the different user types that might use your service, product, site, or brand in a similar way.
Communication Goal – What does the company/brand want to communicate?
Emotional Mindset – What’s the mindset of the user? For example, in a retail context it could be: Needs Validation, Got to Be First/Early Adopter, Buy and Be Done with it, etc.
By incorporating these aspects into your metadata and tagging strategy allows you to delivery empathetic content to specific audiences.
Source for Emotional Mindset examples: Smith Report: Emotional Drivers of Purchase Decisions, https://smith.co/assets/docs/SMITH-POV-8-modes-of-shopping-report.pdf
Image: NN/g Nielsen Norman Group, https://www.nngroup.com/
Of course, there are additional types of metadata (photo color/treatment) and so on. This is just one slice.
Ultimately, you need to put yourself in your customer’s shoes.
Focus on qualitative methods, including: Walking the customer journey(s) Stories Qualitative Metrics Sentiment Focus Groups Interviews Customer support/call center feedback Sales team input, for B2C and B2B
This will help you direct your tagging strategy and help you to course correct when needed.
A bit more definition on AI:
Artificial intelligence can be loosely interpreted to mean incorporating human intelligence to machines. Whenever a machine completes tasks based on a set of stipulated rules that solve problems (algorithms), such an “intelligent” behavior is what is called artificial intelligence.
Machine learning can mean empowering computer systems with the ability to “learn”.
Deep learning is a subset of ML; in fact, it’s simply a technique for realizing machine learning. In other words, DL is the next evolution of machine learning.
Affective AI - aims to create artificial intelligence that recognizes and responds to our moods, emotions, facial expressions, vocal undertones and other nonverbal cues
Images can be slippery and unreliable. Full of contradictions.
The apple with a title of “This is Not an Apple.”
The water feature that could be mistaken for a syringe.
Interpretation of an emotional state: laughing so hard you are crying.
As humans, with our experience – we have a nuanced interpretation of these images. AI cannot always discern these differences.
Image (Girl): https://commons.wikimedia.org/wiki/File:When_was_the_last_time.jpg Apple (This is Not and Apple, Rene Magritte): https://www.wikiart.org/en/rene-magritte/this-is-not-an-apple-1964 Water Feature: https://www.theverge.com/2019/7/19/20700481/ai-machine-learning-vision-system-naturally-occuring-adversarial-examples
Datasheets can vary in quality. What were the decision criteria? Who is accountable?
Breadth vs. Depth More does not always equal better. Current data may not be representative of future trends. Historically underrepresented factors may increase bias.
Training and review is super important. We need an ability to explain the outcomes of AI and related unintended consequences. Organizations struggle to accept responsibilities for processes they cannot understand or control.
All of this increases cost and is a potential barrier to entry.
ImageNet contained a federated database of several datasets for AI – some more problematic than others. Included objects as well as people. The way it classified people was very problematic – as discovered as part of an art project: ImageNet Roulette. ImageNet Roulette revealed ways in which people were tagged with racist and highly offensive terms: Flop, Kleptomaniac, Wanton, Tosser. An images of a woman asleep in an airplane seat, right arm protectively around her pregnant stomach = snob. Included many other misogynistic and racist terms, which I will not repeat here. Many of these images (objects and people) were categorized by Amazon Mechanical Turk workers – low paid, crowed sourced labor, whose work ultimately affect the AI they were helping to create. While some of the more problematic datasets were removed, it did not fully address the underlying problem. Lack of transparency, understanding of process and governance.
References: Excavating AI (on the ImageNet Roulette project): https://www.excavating.ai/ How AI Selfie App ImageNet Roulette Took the Internet by Storm: https://frieze.com/article/how-ai-selfie-app-imagenet-roulette-took-internet-storm
From Excavating AI
At the left, a categorization of cognitive biases, in four broad buckets. What should we remember. Too much information Need to act fast. Not enough meaning.
At the right, one section of the codex. “We are drawn to details that confirm our own existing beliefs” – including: Confirmation Bias Post-purchase rationalization (my favorite, all those shoes!) Selective perception Experimenter’s bias Observer effect Ostrich effect And more . . .
Right now, I don’t think tagging for empathy at scale is really practical. Unless there are a lot of resources behind this sort of project.
In the future – potentially.
Training datasets are critical and require transparency, understanding and rigor. AI system will also need the high level of transparency into process, decision-making into application of terms. Governances is so very important here.
In considering bias – I think we should own our bias and be transparent. Include it as part of our dataset and metadata. While we can be more neutral – we cannot be completely neutral. So, instead of pretending to be neutral – let’s just admit to our bias. It is now part of the context.
Can AI Effectively Tag for Empathy: A Discussion on Artificial Intelligence and the Perception of Empathy
CAN AI EFFECTIVELY
TAG FOR EMPATHY?
DAM Practitioners’ Summit
Director, Content Experience
A Discussion on Artificial Intelligence and
the Perception of Empathy.